Capturing implementation knowledge: applying focused ethnography to study how implementers generate and manage knowledge in the scale-up of obesity prevention programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Bespoke electronic information management systems are being used for large-scale implementation delivery of population health programs. They record sites reached, coordinate activity, and track target achievement. However, many systems have been abandoned or failed to integrate into practice. We investigated the unusual endurance of an electronic information management system that has supported the successful statewide implementation of two evidence-based childhood obesity prevention programs for over 5 years. Upwards of 80% of implementation targets are being achieved. METHODS: We undertook co-designed partnership research with policymakers, practitioners, and IT designers. Our working hypothesis was that the science of getting evidence-based programs into practice rests on an in-depth understanding of the role programs play in the ongoing system of local relationships and multiple accountabilities. We conducted a 12-month multisite ethnography of 14 implementation teams, including their use of an electronic information management system, the Population Health Information Management System (PHIMS). RESULTS: All teams used PHIMS, but also drew on additional informal tools and technologies to manage, curate, and store critical information for implementation. We identified six functions these tools performed: (1) relationship management, (2) monitoring progress towards target achievement, (3) guiding and troubleshooting PHIMS use, (4) supporting teamwork, (5) evaluation, and (6) recording extra work at sites not related to program implementation. Informal tools enabled practitioners to create locally derived implementation knowledge and provided a conduit between knowledge generation and entry into PHIMS. CONCLUSIONS: Implementation involves knowing and formalizing what to do, as well as how to do it. Our ethnography revealed the importance of hitherto uncharted knowledge about how practitioners develop implementation knowledge about how to do implementation locally, within the context of scaling up. Harnessing this knowledge for local use required adaptive and flexible systems which were enabled by informal tools and technologies. The use of informal tools also complemented and supported PHIMS use suggesting that both informal and standardized systems are required to support coordinated, large-scale implementation. While the content of the supplementary knowledge required to deliver the program was specific to context, functions like managing relationships with sites and helping others in the team may be applicable elsewhere.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it