Will E-Monitoring of Policy and Program Implementation Stifle or Enhance Practice? How Would We Know?
Bibliographic record
Abstract
Electronic or digital monitoring systems could promote the visibility of health promotion and disease prevention programs by providing new tools to support the collection, analysis, and reporting of data. In clinical settings however, the benefits of e-monitoring of service delivery remain contested. While there are some examples of e-monitoring systems improving patient outcomes, the smooth introduction into clinical practice has not occurred. Expected efficiencies have not been realized. The restructuring of team work has been problematic. Most particularly, knowledge from research has not advanced sufficiently because the meaning of e-monitoring has not been well theorized in the first place. As enthusiasm for e-monitoring in health promotion grows, it behooves us to ensure that health promotion practice learns from these insights. We outline the history of program monitoring in health promotion and the development of large-scale e-monitoring systems to track policy and program delivery. We interrogate how these technologies can be understood, noticing how they inevitably elevate some parts of practice over others. We suggest that progress in e-monitoring research and development could benefit from the insights and methods of improvement science (the science that underpins how practitioners attempt to solve problems and promote quality) as conceptually distinct from implementation science (the science of getting particular evidence-based programs into practice). To fully appreciate whether e-monitoring of program implementation will act as an aid or barrier to health promotion practice we canvass a wide range of theoretical perspectives. We illustrate how different theories draw attention to different aspects of the role of e-monitoring, and its impact on practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.023 | 0.056 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".