An intersectoral network for chronic disease prevention: the case of the Alberta Healthy Living Network
Bibliographic record
Abstract
Chronic Diseases (CDs) are the leading causes of death and disability worldwide. CD experts have long promoted the use of integrated and intersectoral approaches to strengthen CD prevention efforts. This qualitative case study examined the perceived benefits and challenges associated with implementing an intersectoral network dedicated to CD prevention. Through interviewing key members of the Alberta Healthy Living Network (AHLN, or the Network), two overarching themes emerged from the data. The first relates to contrasting views on the role of the AHLN in relation to its actions and outcomes, especially concerning policy advocacy. The second focuses on the benefits and contributions of the AHLN and the challenge of demonstrating non-quantifiable outcomes. While the respondents agreed that the AHLN has contributed to intersectoral work in CD prevention in Alberta and to collaboration among Network members, several did not view this achievement as an end in itself and appealed to the Network to engage more in change-oriented activities. Managing contrasting expectations has had a significant impact on the functioning of the Network.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".