Theoretical conceptions of intervention research addressing cancer control issues
Bibliographic record
Abstract
Population health intervention research (PHIR) involves the use of scientific methods to produce knowledge about policy and program interventions that operate within or outside of the health sector and have the potential to impact health at the population level. PHIR is a relatively new research field that has gained momentum internationally. When developing PHIR, it is important to have a program theory with the potential to increase intervention success by identifying underlying mechanisms, areas of failure and unintended outcomes. Since 2010, the French National Cancer Institute (Institut National du Cancer-INCa) has supported a national, competitive, dedicated call for proposals in PHIR to tackle cancer control issues. After 5 years of activity, specific analysis of the proposals submitted for funding and/or funded (n = 63) from descriptive and analytic perspectives was called for. Analysis of the data revealed diversity in terms of targeted populations, partnerships engaged and methodological approaches. Projects were more likely to be funded (n = 15) if presented with a robust methodological approach and diversity in methodology, and/or with research objectives at different levels of action. The analysis also revealed that researchers do not explicitly describe theoretical constructs underpinning their interventions to combat cancer. PHIR still needs improvement to better incorporate social, institutional and policy approaches to cancer control. Researchers should apply a theory-driven approach to distinguish between 'program failure' and 'theory failure'. Following up the funded projects will allow successes and failures to be evaluated with respect to the use (or non-use) of theory-driven approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".