Population health intervention research: the place of theories
Bibliographic record
Abstract
BACKGROUND: An international workshop on population health intervention research (PHIR) was organized to foster exchanges between experts from different disciplines and different fields. This paper aims to summarize the discussions around some of the issues addressed: (1) the place of theories in PHIR, (2) why theories can be useful, and (3) how to choose and use the most relevant of them in evaluating PHIR. METHODS: The workshop included formal presentations by participants and moderated discussions. An oral synthesis was produced by a rapporteur to validate, through an expert consensus, the key points of the discussion and the recommendations. All discussions were recorded and have been fully transcribed. RESULTS: The following recommendations were generated through a consensus in the workshop discussions: (i) The evaluation of interventions, like their development, could be improved through better use of theory. (ii) The referenced theory and framework must be clarified. (iii) An intervention theory should be developed by a partnership of researchers and practitioners. (iv) More use of social theory is recommended. (v) Frameworks and a common language are helpful in selecting and communicating a theory. (vi) Better reporting of interventions and theories is needed. CONCLUSION: Theory-driven interventions and evaluations are key in PHIR as they facilitate the understanding of mechanisms of change. There are many challenges in developing the most appropriate theories for interventions and evaluations. With the wealth of information now being generated, this subject is of increasing importance at many levels, including for public health policy. It is, therefore, timely to consider how to build on the experiences of many different disciplines to enable the development of better theories and facilitate evidence-based decisions.
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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: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | 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 | 0.258 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.128 |
| Scholarly communication | 0.035 | 0.036 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 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".