Developing a workbook to support the contextualisation of global health systems guidance: a case study identifying steps and critical factors for success in this process at WHO
Bibliographic record
Abstract
BACKGROUND: Global guidance can help countries strengthen their health systems to deliver effective interventions to their populations. However, to have an impact, guidance needs to be contextualised or adapted to local settings; this process includes consideration of health system arrangements and political system factors. To date, methods to support contextualisation do not exist. In response, a workbook was designed to provide specific methods and strategies to enable the contextualisation of WHO's 'Optimizing health worker roles to improve maternal and newborn health' (OptimizeMNH) guidance at the national or subnational level. The objective of this study was to describe the process of developing the workbook and identify key steps of the development process, barriers that arose and facilitators that helped overcome some of these barriers. METHODS: A qualitative single case study design was carried out. Interviews, documents and a reflexive journal were used. Constant comparison and an edit-style of organisation were used during data analysis to develop concepts, themes, subthemes and relationships among them. RESULTS: Thirteen interviews were conducted and 52 documents were reviewed. Three main steps were identified in the process of developing the workbook for health systems guidance contextualisation, namely (1) determining the need for and gaining approval to develop the workbook, (2) developing the workbook (taking on the task, creating the structure of the workbook, operationalising its components, undergoing approval processes and editing it), and (3) implementing the workbook both at the WHO level and at the national/subnational level. Five barriers and/or facilitators emerged relevant to each step, namely (1) having well-placed and credible champions, (2) creating and capitalising on opportunities, (3) finding the right language to engage various actors and obtain buy-in, (4) obtaining and maintaining meaningful buy-in, and (5) ensuring access to resources. CONCLUSIONS: Understanding the key steps and the critical factors involved in the process of developing the workbook could help in the planning of similar and other tools aimed to support the implementation of WHO guidance. A plan for dissemination and implementation needs to be addressed during the preparation of these tools.
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.051 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".