Building the field of population health intervention research: The development and use of an initial set of competencies
Bibliographic record
Abstract
Population health intervention research (PHIR) is a relatively new research field that studies interventions that can improve health and health equity at a population level. Competencies are one way to give legitimacy and definition to a field. An initial set of PHIR competencies was developed with leadership from a multi-sector group in Canada. This paper describes the development process for these competencies and their possible uses. Methods to develop the competencies included key informant interviews; a targeted review of scientific and gray literature; a 2-round, online adapted Delphi study with a 24-member panel; and a focus group with 9 international PHIR experts. The resulting competencies consist of 25 items grouped into 6 categories. They include principles of good science applicable though not exclusive to PHIR, and more suitable for PHIR teams rather than individuals. This initial set of competencies, released in 2013, may be used to develop graduate student curriculum, recruit trainees and faculty to academic institutions, plan non-degree professional development, and develop job descriptions for PHIR-related research and professional positions. The competencies provide some initial guideposts for the field and will need to be adapted as the PHIR field matures and to meet unique needs of different jurisdictions.
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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 | 0.198 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".