Embedded health service development and research: why and how to do it (a ten-stage guide)
Bibliographic record
Abstract
In a world of changing disease burdens, poor quality care and constrained health budgets, finding effective approaches to developing and implementing evidence-based health services is crucial. Much has been published on developing service tools and protocols, operational research and getting policy into practice but these are often undertaken in isolation from one another. This paper, based on 25 years of experience in a range of low and middle income contexts as well as wider literature, presents a systematic approach to connecting these activities in an embedded development and research approach. This approach can circumvent common problems such as lack of local ownership of new programmes, unrealistic resource requirements and poor implementation.We lay out a ten-step process, which is based on long-term partnerships and working within local systems and constraints and may be tailored to the context and needs. Service development and operational research is best prioritised, designed, conducted and replicated when it is embedded within ministry of health and national programmes. Care packages should from the outset be designed for scale-up, which is why the piloting stage is so crucial. In this way, the resulting package of care will be feasible within the context and will address local priorities. Researchers must be entrepreneurial and responsive to windows of funding for scale-up, working in real-world contexts where funding and decisions do not wait for evidence, so evidence generation has to be pragmatic to meet and ensure best use of the policy and financing cycles. The research should generate tested and easily usable tools, training materials and processes for use in scale-up. Development of the package should work within and strengthen the health system and other service delivery strategies to ensure that unintended negative consequences are minimised and that the strengthened systems support quality care and effective scale up of the package.While embedded development and research is promoted in theory, it is not yet practiced at scale by many initiatives, leading to wasted resources and un-sustained programmes. This guide presents a systematic and practical guide to support more effective engagements in future, both in developing interventions and supporting evidence-based scale-up.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.046 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".