Evidence-informed health policy 4 – Case descriptions of organizations that support the use of research evidence
Bibliographic record
Abstract
BACKGROUND: Previous efforts to produce case descriptions have typically not focused on the organizations that produce research evidence and support its use. External evaluations of such organizations have typically not been analyzed as a group to identify the lessons that have emerged across multiple evaluations. Case descriptions offer the potential for capturing the views and experiences of many individuals who are familiar with an organization, including staff, advocates, and critics. METHODS: We purposively sampled a subgroup of organizations from among those that participated in the second (interview) phase of the study and (once) from among other organizations with which we were familiar. We developed and pilot-tested a case description data collection protocol, and conducted site visits that included both interviews and documentary analyses. Themes were identified from among responses to semi-structured questions using a constant comparative method of analysis. We produced both a brief (one to two pages) written description and a video documentary for each case. RESULTS: We conducted 51 interviews as part of the eight site visits. Two organizational strengths were repeatedly cited by individuals participating in the site visits: use of an evidence-based approach (which was identified as being very time-consuming) and existence of a strong relationship between researchers and policymakers (which can be challenged by conflicts of interest). Two organizational weaknesses - a lack of resources and the presence of conflicts of interest - were repeatedly cited by individuals participating in the site visits. Participants offered two main suggestions for the World Health Organization (and other international organizations and networks): 1) mobilize one or more of government support, financial resources, and the participation of both policymakers and researchers; and 2) create knowledge-related global public goods. CONCLUSION: The findings from our case descriptions, the first of their kind, intersect in interesting ways with the messages arising from two systematic reviews of the factors that increase the prospects for research use in policymaking. Strong relationships between researchers and policymakers bodes well given such interactions appear to increase the prospects for research use. The time-consuming nature of an evidence-based approach, on the other hand, suggests the need for more efficient production processes that are 'quick and clean enough.' Our case descriptions and accompanying video documentaries provide a rich description of organizations supporting the use of research evidence, which can be drawn upon by those establishing or leading similar organizations, particularly in low- and middle-income countries.
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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 | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.071 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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.
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".