The fundamentals of cross-sector collaboration for social change to promote population health
Bibliographic record
Abstract
Cross-sector collaboration is increasingly relied upon to tackle society's pressing and intractable problems. Chief among societal problems are unfavorable structural and social determinants of health. The ability to positively change these health determinants rests on the collaborative processes and structures of governance across diverse sectors in society. The purpose of this article is to present a conceptual framework that sheds light on the basic requirements of cross-sector collaboration for social change to promote the health of populations. A search for theoretical articles on cross-sector collaboration in the fields of public administration and public health was conducted within the journal databases ABI/INFORM Complete and MEDLINE. This search strategy was supplemented by an internet search of the grey literature for high-profile models of cross-sector collaboration. The conceptual framework builds on previous scholarly work by placing emphasis on five essential conditions for collective impact, and on the pivotal role of collective learning. Collective learning, at the basis of planning and taking action, is at the core of effective cross-sector initiatives, specifically because of its critical role in constantly adapting strategies to changing circumstances and unanticipated situations within complex socio-ecological systems.
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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".