Evidence-Based Refinement of Health and Social Services
Bibliographic record
Abstract
To promote evidence-based refinement of quality health and social services delivery and care, decision makers, researchers, and practitioners often undertake intervention research. Intervention research tests and describes new strategies for achieving desired outcomes. But theoretical, methodological, and practical issues continue to plague even alternative participatory approaches to intervention research, raising questions about its potential for promoting quality health and social services and care. In response to this persistent challenge, the authors of this article propose a radical solution, namely intravention research, laying out its unique features as well as its theoretical and practical implications. Their conceptualization sets the stage for dialogue on options for advancing research methodologies and methods that might better promote evidence-informed health and social services.
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 | 0.473 | 0.518 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".