Supersetting-tilgangen til integreret sundhedsfremme i lokalsamfundet
Bibliographic record
Abstract
Dette kapitel beskriver en nyudviklet og helhedspræget interventionsstrategi, supersetting-tilgangen, til udvikling, implementering og evaluering af komplekse sociale og sundhedsfremmende interventioner i lokalsamfundet. Forfatterne adresserer gennem settings-perspektivet den sociale ulighed i sundhed, som har været stigende gennem de seneste 30 år. Supersetting-tilgangen inddrager mange forskelligartede ressourcer, materielle såvel som ikkematerielle, og den beskrives som en interventionsstrategi rettet mod et fælles defineret mål. Kapitlet beskriver en række initiativer og samskabelsesprocesser, som involverer beboere og professionelle aktører, f.eks. fra den offentlige sektor, det private erhvervsliv, civilsamfundet og akademiske miljøer. Teoretisk har begrebet sin baggrund i WHO's strategier for sundhedsfremme, "Health for All" fra 1980 (WHO, 2010) og "The Ottawa Charter for Health Promotion" fra 1986 (WHO, 2009). Kapitlet præsenterer et empirisk nedslag i form af Projekt SOL – Sundhed og Lokalsamfund, som et dansk eksempel på et supersetting-sundhedsfremmetiltag. Kapitlet ønsker at formidle supersetting-tilgangen og skabe refleksion over, hvordan den anvendes til at reducere den sociale ulighed i Danmark.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.021 |
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".