2  Timekeeping or Scorekeeping

The administrative office of our provincial CDC is quiet at five in the morning. On the desk, a printed list of names lies next to a keyboard. A stack of blank transport medium tubes sits in their plastic racks, clean and empty. Beside them, the barcode printer waits, its green status light blinking slow. I am wearing my ordinary clothes, a faded cotton shirt and trousers. There are no clinical samples in this room yet, so there is no need for a hazmat suit, heavy blue gown, or double layers of gloves. But as I look at the paper list under the fluorescent tube, a quiet realization comes over me. This desk work is the most dangerous step of the entire pandemic response.

I open the email that arrived an hour ago. The messaging app on my phone is already buzzing with new lists sent by district teams. There are names of citizens quarantined in facilities after arriving from overseas, names of prisoners from the provincial jail, and names of people who died overnight in their homes. I begin transcribing them. My fingers move across the keys, converting the text into a structured Excel file. I connect the file to the barcode software and hit print. The machine hums, spitting out small, sticky labels with black-and-white lines. I peel each one and wrap it around a blank plastic tube. This barcode is a unique identifier, a digital thread that will bind this patient’s identity to their specimen from the moment of collection to the final diagnostic result.

Outside this office, everyone associates our response with the drama of biological hazards. They think of the ambulance engine starting up, or whatever vehicle our unit could find to transport the teams. They think of the cold boxes, foam or ordinary ice, waiting in rows along the wide corridor outside. Yet, we cannot go out to collect a single sample until these labels are ready. If a barcode is printed with a wrong digit or stuck to the wrong tube, a patient’s result is lost, and the chain of containment breaks.

We used to move fewer than three hundred samples a day, and the ceiling was this desk, not the bench. With identifiers fixed here before anything goes to the field, the same team can put four thousand through the day. The arithmetic is not mysterious and it is worth setting out, because a barcode printer plainly does not make a thermocycler faster. Each reaction well holds one tube, so when every person needs their own well, the number of people tested in a day can never exceed the number of wells run. Pooling breaks that equivalence: several people share one well, and one run now covers several times as many people. What had made pooling impractical was the paperwork, since somebody had to write out by hand which names sat in which pool and then write them out again to retest a positive one. Once the identifiers were fixed at the desk, that writing disappeared, and the pool ratio became a dial we could turn against the positivity rate that week. The throughput did not come from the printer. It came from removing the clerical work that had made the instruments’ real capacity unreachable. The ceiling moved off the desk and onto the bench, where it belongs. Same staff. No new budget line. This administrative routine is still running in our department today. I finish pasting the last label. The plastic tubes are now ready to be packed into the cold boxes. I look down at my hands, still free of gloves. The real bottleneck of the crisis is not happening in a secure laboratory or a quarantine ward. It is happening here, at a wooden desk, in everyday clothes, under the hum of an office light.

In those five years, I did not just handle samples. I watched how people responded to the systems around them. During my time in the laboratory, I observed a fundamental truth about data. The exact same set of records can produce two completely opposite human reactions. It all depends on who owns that data. Under one frame, which we can call the timekeeping mindset, workers feel watched and monitored. They see the system as an intrusive eye. It is a tool of surveillance that tracks their minutes and counts their mistakes. When people feel controlled, they do not want to cooperate. They hide their efforts, they resist, and they do only the bare minimum required to avoid trouble. But there is another frame. If you shift the design so that workers are building their own records, the entire dynamic changes. Under this framework, the data belongs to them. It is their point-accumulation system, a physical ledger of their own sweat and labor. When workers know that they own the record of their own work, they do not hide. They actively step forward to volunteer the evidence of their achievements. They want to log every hour, every field visit, and every test because they are building a professional asset that belongs to them.

This dramatic shift in behavior is not just an observation from our laboratory floor. It is explained by Self-Determination Theory, a psychological framework that highlights our basic needs for autonomy and competence. While researchers consider the core psychological mechanism of this theory to be strong, they rate its direct application in workplace systems as moderate. According to this theory, the way we frame a system changes everything. The theory draws a sharp distinction between informational rewards and controlling rewards. Informational rewards are those that confirm our competence and acknowledge our skills. When a worker receives feedback that confirms their mastery, their intrinsic motivation rises. They feel a sense of pride and professional growth. Controlling rewards, on the other hand, are designed to pressure, monitor, and push people. They are tools of control that signal to the worker that they are under constant surveillance. When a system relies on controlling rewards and pressure, motivation drops. This psychological reality tells us something vital about system design. The way we frame a system is not a minor cosmetic detail. It is a core design variable. How we structure the flow of data is what determines whether the system succeeds or fails.

In my proposal, I am not asking for a more expensive system to watch our staff. I am proposing that we change the frame entirely. We need to stop using data to monitor people and start letting people use data to build themselves up. When we make this shift, we transform invisible field labor into a verified professional asset. This is the difference between a system of surveillance and a ladder of opportunity.

I am writing this proposal because I know what it is like to work in the shadows, where your sweat has no physical record. Many people think that motivating health workers requires complex schemes or expensive campaigns. But as I sat at my desk and looked at the global literature on human behavior, I discovered the strongest evidence for our proposal. It shows that the simple act of tracking your own progress is a powerful force.

This is not a vague claim. In 2016, Harkin and his colleagues published a landmark meta-analysis in Psychological Bulletin. They compiled data from 138 independent studies, representing roughly 19,951 participants. Their findings were clear. They found that monitoring progress alone raised goal attainment. It did so with a substantial effect size of 0.40. But there was a vital detail in their findings that speaks directly to my experience in the laboratory. They discovered that this positive effect was even larger when progress was physically recorded. It was also larger when that recorded progress was reported or made public. This is the true pillar of our entire design. The value of this system does not lie in trivial gamified badges or superficial point systems. The value lies in the physical, verified recording of a worker’s daily labor. It is the act of turning invisible actions into a permanent record (1–3).

One limit belongs with that finding before I lean on it. Harkin and colleagues were synthesising studies of people pursuing their own goals: losing weight, giving up cigarettes, keeping to a course of study. Nobody in those trials was building a record that an employer, an auditor or a scholarship board would later read. Carrying the result across to a high-stakes workplace log is an extrapolation. The mechanism is plausible and the direction is probably right, but it has not been tested where the record decides something about the person keeping it. (3)

We must look at how this physical record changes how a worker feels about their employer. In our field, people walk away because they feel completely invisible and unsupported. This feeling is captured by a concept called Perceived Organizational Support, or POS. In 2017, Kurtessis and his colleagues published a massive meta-analysis in the Journal of Management. They looked at how this sense of support correlates with employee behavior. The numbers they found are remarkably strong. A worker’s perceived organizational support has a massive positive correlation of 0.69 with their commitment to the organization. When staff feel supported, their intention to leave drops significantly, with a negative correlation of minus 0.50. Their actual rate of quitting drops as well, with a correlation of minus 0.21 (4).

But how do we build that sense of support in a provincial laboratory? The meta-analysis by Kurtessis and his team found that development opportunities are among the strongest sources of perceived organizational support. Giving people a chance to grow is far more powerful than temporary bonuses (4). This is why our proposal links a verified record of service to tangible educational gateways like scholarships. We are not just printing labels or tracking hours for surveillance. We are creating a physical record of competence that a worker can use to claim a professional future.

That sentence contains the most dangerous assumption in this book, and I would rather name it than let a reviewer find it. A ledger does not create a scholarship. It records what somebody did; it does not fund a place for them to study. If a laboratory installs the record, tells its technicians that verified service opens a door, and then no door opens because no institution ever agreed to honor it, we will have built the precise broken contract that Rousseau describes, and we will have done it deliberately, with better record-keeping than before (5). That outcome is worse than doing nothing, and it is the likely outcome if the sequence is run in the wrong order.

So the sequence matters more than the software. The scholarship commitment has to exist first, in writing, from a body that can actually admit students, before a single technician is told that the log leads anywhere. Until that letter exists, the record should be described to staff as exactly what it is and nothing more: a document of their own work, useful for their own curriculum vitae, owned by them. That framing is honest at any funding level and survives the pilot failing. Anything beyond it is a promise, and a promise is not ours to make until somebody with a seat at a training institution has signed for it (5). When we do this, we transform the daily grind into a visible ladder of growth, backed by the strongest psychological science we have (4).

At the time I often heard suggestions from visiting consultants about how to motivate our staff. They frequently proposed setting up leaderboards. They wanted us to display a public ranking of who processed the most sample tubes or who logged the most hours in the field. They believed that public competition would naturally drive us to work harder and faster. In my proposal, however, you will find no leaderboards. I have intentionally excluded them from the design of this recognition pathway. This decision is not a matter of personal taste. It is guided by what actually happens when you introduce ranking systems to frontline health workers.

We should look at the real-world evidence from field research. A recent study on the design of gamified training systems for community health workers found something very clear. Frontline workers do not read public ranking systems as a source of healthy motivation. Instead, they read them as tools of surveillance. When you introduce public scores, it creates a high level of anxiety. It breeds resistance and tension inside a professional environment where people must rely on one another to survive. In our laboratories and field units, we do not work in isolation. We work in tight, interdependent teams.

Furthermore, the broader literature on gamification supports this caution. A systematic review by Sailer and Homner in 2020 analyzed the actual effectiveness of these game-like elements. They found that competition on its own produced smaller gains in learning outcomes and in behavioural measures than competition paired with a cooperative element, and I should be precise that those are the outcomes measured, not retention or accuracy at a workbench. When you force people to compete against their colleagues without any cooperative element, the team dynamics break down. Even more importantly, their meta-analysis showed that when you look at high-rigor studies, most of the grand claims about gamification begin to fade. They looked at cognitive, motivational, and behavioral effects. Only the cognitive effect of gamification actually held up under high-rigour analysis. The behavioral and emotional changes that marketers promise simply do not survive strict scientific scrutiny (6).

We must be honest about what we do not know. There is currently no randomized controlled trial evaluating the use of points, badges, or leaderboards for community health worker performance in low and middle-income countries. The trials simply do not exist. What has been documented to work in these settings is financial incentives tied directly to verified activities. Public rankings, on the other hand, are what consistently backfire.

For these reasons, the pathway I am proposing relies on self-competition. People should compete only against their own past record, not against their colleagues. A worker’s dashboard will show their own progress, their own verified hours, and their own path toward their educational goals. It will not show where they stand in a public hierarchy. We want to build a tool that confirms competence and records sweat, not a scoreboard that publicizes shame. When we remove the leaderboard, we remove the feeling of being watched, and we allow workers to focus on their own growth.

I filled out my share of paperwork. We would often sit down at the end of the month, try to remember what we had done weeks earlier, and write it down on paper sheets. But as I write this proposal, I must be completely honest about this practice. A paper log submitted at the end of the month is not actual scientific evidence of work. It is a statement. It is a retrospective claim, a recollection of events that is highly prone to human error and fading memory. Real data integrity requires something much more rigorous.

The global pharmaceutical and medical industries have already named the standard we must follow. It is called ALCOA. This standard was originally coined by Stan Woollen at the United States Food and Drug Administration in the early 1990s. He created it as a simple mnemonic device to train inspectors. Over time, through widespread adoption across clinical trials and manufacturing, this mnemonic became the global gold standard for data integrity (7).

It is important to note a vital caveat here. These standards were written for regulated pharmaceutical manufacturing and clinical laboratories. None of them were originally designed for a provincial preventive medicine lab in Vietnam. Applying them in our provincial CDC is a deliberate borrowing of an industry standard, not a legal requirement. Yet, if we want our records to be trusted internationally, this is the bar we must meet.

The very first letter in this standard stands for Attributable. To be attributable, a record must show exactly who performed an action and when they did it. This information must be tied directly to a unique individual identity. In many provincial laboratories, staff often share a single computer login or sign off on sheets using a generic team name. This is a classic inspection finding because shared logins completely break accountability. If everyone is logged in as the same user, the trace is gone. You cannot verify who actually ran a test or who made a change.

The third letter in the standard stands for Contemporaneous. This means recording the data at the exact moment the work is performed. Writing logs late, keeping rough notes on scraps of paper to copy them later, or batching data entry at the end of the day is not a minor paperwork error. In the eyes of international inspectors, it is a direct violation of data integrity.

This is why we need to build a digital pathway that records work as it happens. When a technician scans a barcode on a container tube, the database must log that action instantly with their unique identity. We cannot rely on retrospective paper statements compiled weeks after the fact. I have to secure a contemporaneous, attributable record. That is the only way to transform our invisible daily sweat into a verified professional asset that outside institutions can trust.

I saw how easily human memory fails when it is forced to do paperwork weeks after the fact. In this proposal, I want to address the third core letter of the ALCOA standard: Contemporaneous. In the world of high-standard data integrity, contemporaneous means that an activity must be recorded at the exact moment it happens. It cannot wait. When recording happens hours or weeks after the work, certain practices appear everywhere, and they appear for the same reason. Logs get completed after the fact. Numbers get written on whatever paper is at hand and transcribed into the official ledger later. Entry gets batched to the end of a long day. None of this comes from dishonesty. It comes from a system that was designed to make people remember instead of designed to let them record. Under international data standards these practices are treated as data integrity failures, and the standards are right about the consequence: the record stops being trustworthy. But the fix does not belong to the person holding the pen. It belongs to whoever designed the moment at which the pen is picked up.

To prevent these failures, we must design a system that complies with the highest regulatory rules, like the United States Food and Drug Administration’s 21 CFR Part 11. Specifically, section 11.10(e) requires a computer-generated, time-stamped audit trail that records every creation, modification, or deletion. The rule states that any change must not obscure previously recorded information. Compliant laboratory systems do meet that rule, with append-only audit tables, database controls and digital signatures. What they generally do not add is cryptographic immutability that holds against the system’s own administrator, and no rule requires them to. The audit trail answers to the institution that runs it. This means that if we build a system where a record is pushed immediately to a server and secured unalterably using cryptography, we are actually performing above the current regulatory standard rather than merely catching up to it.

There is a limit to what this technology can do, and it is worth naming before anyone gets excited about cryptography. A tamper-proof log of fabricated entries is still a tamper-proof lie. Cryptography cannot prevent a technician from lying about a test they never ran.

Because of this limitation, the whole idea of our digital design is mistake-proofing. In manufacturing, they call this poka-yoke. We should not waste our time telling stressed, overworked frontline staff not to do things wrong. We should not rely on constant warnings or heavy training manuals. Instead, we should build a workflow where the easy path is the correct one. When a technician scans a barcode, the time and the identity are fixed at that moment, so writing the entry up from memory a week later stops being the convenient option. I want to be exact about the limit here, because it matters. A scan proves that a label was read at a particular second. It does not prove that a specimen was drawn, and nothing stops somebody scanning fifty tubes at a desk. Contemporaneous capture removes the drift that comes from writing things down later. It is not a defence against someone who has decided to lie, and the later layers exist because of that. By securing the data at the moment of creation, we make integrity the only path forward.

I also learned that you cannot build a sustainable health system on guesswork. When we design tools to motivate frontline workers, we must rely on honest, proven mechanics from behavioral research rather than marketing hype. Our proposal does not try to invent human behavior. Instead, we want to look at how people actually respond to progress and thresholds, using established science while remaining completely honest about its limits.

The first behavioral concept we must build upon is the endowed progress effect. In 2006, researchers Nunes and Drèze published a field experiment studying how people complete tasks. They compared two scenarios. In the first, people received a loyalty card with eight blank spaces. In the second, they received a ten-space card that already had two pre-stamped marks. Technically, both groups needed to complete exactly eight tasks to reach the goal. Yet, the group with the pre-stamped cards showed a much higher completion rate, rising from 19 percent to 34 percent. The feeling of already being on a journey is a powerful psychological driver. However, we must preserve a vital caveat from this study. This pre-stamped progress only works if there is a stated, logical justification for the endowment. If you give people points arbitrarily without explaining why they earned them, the effect weakens significantly. Furthermore, there has been no strong, independent replication of this experiment. We should trust the general direction of this effect, but we must never treat these exact percentages as absolute laws (8,9).

The second mechanic is the goal gradient effect, documented in 2006 by Kivetz, Urminsky, and Zheng. Using multiple research methods, they proved that people naturally accelerate their efforts as they get closer to a threshold. In their study, participants accelerated their rate of progress by roughly 20 percent as they neared the goal. But their research also documented a critical post-reward slump. Right after a person receives their reward, their motivation drops, and their progress slows down significantly. This post-reward slowdown is a major risk for health systems. If we build a system with a single, one-shot threshold, our staff might work hard to reach it and then immediately stop. To prevent this, we must design continuous thresholds rather than one-shot targets. The ladder of professional development must keep going so that there is always another step within reach (10).

Finally, we must acknowledge the ethical boundary of these mechanics. These effects were originally designed to sell consumer goods and increase retail purchases. The right way to apply them in public health is to help workers see their own hard work accumulate as a genuine ladder of growth. They must never become a device for extracting unpaid labor from underpaid staff. By combining these honest mechanics, we can create a system where workers feel they have already started their journey, and where they can safely build a professional future.

I am writing this proposal because I know how a poorly designed system can crush a team. When people talk about gamifying public health, they often suggest using daily streaks. They want us to log in every single day to keep a digital streak alive, just like popular language learning apps do. But in our proposal, we have completely rejected hard daily streaks. This is a deliberate choice based on both the realities of provincial field work and an honest look at behavioral evidence.

A daily streak system is actively punishing for the people who actually do the work. In a provincial CDC, our schedules are never predictable. We do not work a neat, daily nine-to-five routine. We work exhausting night shifts, and we get pulled into sudden, urgent outbreak responses that last for days. When you are standing in a remote village trying to contain an epidemic, you do not have the time or energy to log into an app just to save a digital streak. Missing a single day because of a real emergency ruins your streak. It wipes out weeks of visible progress. Instead of motivating people, it makes them feel defeated. Because of this, our design throws out daily requirements. We use weekly quotas and forgiveness tokens instead. This gives staff the flexibility to manage their intense, unpredictable workloads without feeling penalized by the system.

The evidence behind daily streaks is thinner still. If you look at the widely quoted statistics about how people lose motivation or quit when a streak is broken, you will find a surprising truth. Those numbers trace back only to marketing blogs. They have no verifiable, peer-reviewed source. It is mostly industry lore, not established science. We cannot build a state system on marketing myths.

This brings me to the second decision. We have structured the data around ALCOA, and I said earlier that borrowing it is a choice rather than an obligation. It is worth saying why we would choose a standard nobody is holding us to. We choose to borrow it because it is the only way to build a record that international partners can trust, but we must acknowledge this choice openly. We are not catching up to a legal mandate. We are choosing a higher path because our staff deserve a record of their labor that is too rigorous for anyone to dismiss. By combining realistic weekly goals with strict data integrity standards, we can build a system that respects the lives of our workers while protecting the truth of their work.

Running assays under pressure taught me how easily ideological debates about motivation can lose touch with reality on the ground. When we design a system to recognize and reward our technicians, we must address the motivation debate honestly rather than picking over extreme academic theories. For decades, experts have argued about whether extrinsic rewards destroy a person’s inner drive. In 1999, Deci, Koestner, and Ryan published a classic meta-analysis showing that tangible, performance-contingent rewards actually reduce free-choice intrinsic motivation, with effect sizes between -0.28 and -0.40, while verbal praise increases it. This study became a major argument against using physical rewards in the workplace. However, the debate did not end there. Gerhart and Fang later counter-argued in 2015 that the actual field evidence for this supposed harm is remarkably thin. They pointed out that laboratory experiments do not reflect the complex, long-term realities of a functioning public health organization (11,12).

The best current reconciliation of this conflict comes from a massive meta-analysis by Cerasoli and colleagues in 2014, published in Psychological Bulletin. Drawing on forty years of work and 183 separate samples, their work shows that intrinsic motivation and external incentives are largely additive rather than opposed. They do not cancel each other out. Instead, they drive different aspects of performance. Intrinsic motivation is a powerful predictor of work quality, while incentives are a strong predictor of work quantity (13).

Therefore, any defensible claim we make in this proposal must remain narrow. We have to reject the dramatic overclaim that money simply destroys professional motivation. That statement is flatly incorrect and unsupported by modern evidence. What the evidence actually shows is that output-contingent money narrows human effort toward what is actively measured, and this focused effort is incredibly fragile once those incentives are withdrawn. In a provincial laboratory, if we only pay for the speed or number of tests processed, our technicians will focus entirely on those metrics and ignore vital duties like instrument calibration, biosafety cleaning, or detailed documentation. Once that temporary money stops, the motivation to perform those measured tasks collapses entirely.

The same absence of trial evidence noted above covers these motivation models too. We are borrowing frameworks from corporate and clinical research in wealthy countries to design tiered scholarships and verified service logs for a Vietnamese provincial laboratory, and the borrowing is the weak joint in the argument. We must not overpromise. By understanding that intrinsic care and structured incentives work together, we can design a registry that honors a technician’s dedication to quality while offering clear, tangible paths for their professional growth.

A well-intentioned gesture of recognition can easily turn toxic. We are proposing a digital registry to track and reward the field contributions of our technicians, but we must understand the precise design conditions that decide whether such a system builds or corrodes our workforce. This is not a matter of administrative preference. It is a matter of documented human behavior, and we must design with these behavioral realities in mind from the very first day.

The first critical condition comes from organizational justice research, specifically the work on procedural fairness by Colquitt and colleagues in two thousand and one. Their research shows that the fairness of the process used to decide who is selected for a reward determines its ultimate impact on the workforce. If the selection process is opaque, secret, or perceived as biased, the reward ceases to be an incentive. Instead, it turns directly into a grievance for those who were passed over, destroying organizational trust and eroding cooperation in the laboratory. For a provincial CDC where teamwork is a matter of biosecurity, an opaque reward is a dangerous hazard. (14)

The second condition is defined by psychological contract research, particularly the framework by Rousseau in 1995. The evidence here is clear and moderate in certainty: promising a professional pathway and then failing to deliver it produces psychological harm that is far worse than never making the promise at all. When an organization violates this psychological contract, staff members experience deep resentment and a collapse in motivation. If we build a registry that promises our technicians a gateway to scholarships, but those gateways are blocked by bureaucratic delays or institutional failures, we will actively destroy the fragile commitment of our frontline staff. (5)

Finally, we must ground our registry in basic needs research, which forms the core mechanism of Self-Determination Theory as outlined by Deci, Olafsen, and Ryan in 2017, and Van den Broeck and colleagues in 2016. While the theoretical mechanism is strong, its practical application carries a moderate level of certainty. This research demonstrates that three basic psychological needs, which are autonomy, competence, and relatedness, each independently predict employee well-being and job attitudes. Crucially, the organizational context itself predicts whether these three needs are met. This means the system around the record matters far more than the record itself. A digital ledger is a tool. If the system surrounding it is coercive, controlling, or competitive, the tool will fail to support our staff (15,16).

I must be completely honest about these findings. These behavioral theories are well-documented in international literature, but there is currently no trial evidence or randomized controlled study evaluating how they apply to preventive health workers in low-resource Vietnamese provincial settings. We are proposing a logical, evidence-informed design to test these hypotheses. By ensuring procedural fairness, honoring our psychological contracts, and focusing on basic psychological needs, we can build a supportive scaffold rather than a corroding trap.

In writing this proposal, I must present the real evidence regarding professional recognition and performance tracking, looking specifically at what works for health workers rather than consumers. Many digital platforms promise miracles based on studies of online shoppers or language learners, but our clinical reality is entirely different. When we examine the actual evidence from healthcare settings, the picture is incredibly narrow.

The strongest single trial evaluating gamified continuing medical education was conducted by Kerfoot and colleagues in 2014. This study ran for fifty-two weeks and tracked more than fourteen thousand patients. Even in this best-case scenario, the trial found only a statistically significant but very small improvement in the time it took to achieve blood pressure control. This is what the absolute ceiling of gamified professional education looks like under rigorous trial conditions. It is a modest, incremental gain, not a revolutionary transformation. (17)

Furthermore, when we look at the systematic review of thirty studies in health professions education conducted by Gentry and colleagues in 2019, the limitations of the current literature become even more obvious. Out of those thirty studies, only two evaluated gamification as such, rather than a single game-like element bolted onto a course. The rest were serious games or simulations. More importantly, the vast majority of the participants in these studies were medical students or residents rather than practicing clinicians who work under the daily, high-pressure constraints of a provincial clinic. We have almost no direct evidence showing how these digital tools affect the behavior of seasoned professionals. (6,15,18)

The gap is widest when we look at community health workers in low-resource settings. Kok and colleagues, reviewing what makes community health worker programmes work across low and middle-income countries, found that predictable packages combining financial and non-financial elements outperform either alone for both retention and performance (19). The review also indicated that publicly displayed recognition improved service delivery, which sits awkwardly beside the refusal of leaderboards set out above, so I should say where the line falls. Recognition that is public and absolute is welcome: a certificate, a named record, a colleague knowing what you did. Recognition that is public and comparative is not: a ranked list that tells a technician they are seventh. The first says the work was seen. The second says somebody else was better, which is a different message and, in the studies cited above, a corrosive one. This is the very narrow, moderate band of evidence that our proposal actually stands on. We are not claiming that digital badges or leaderboards are a proven magic wand. In fact, we must be entirely honest. There is currently absolutely no trial evidence or randomized controlled studies evaluating the impact of leaderboards, digital points, or gamified badges on community health workers in low-income or middle-income countries. Indeed, some design studies suggest that public leaderboards can cause anxiety and destroy collaboration among staff.

Therefore, our proposal is not a grand, speculative bet. We are proposing a modest, highly contained twelve-month pilot of a verified service record to test these behavioral hypotheses in our local context. If we keep our expectations bounded and our design grounded in the real, limited evidence, we can build a functional tool that supports our technicians without overpromising. We need to build on what is proven, not what is hyped.