Great expectations and hard times: developing community indicators in a Healthy Communities Initiative in Canada
Bibliographic record
Abstract
This paper reports on expectations for and community members' experience in the development of community indicators in a healthy communities initiative (HCI) in Alberta, Canada. The HCI process involved community visioning, the creation of action plans to further the vision by addressing key health priorities and/or community capacity building activities and the development of indicators to monitor and report on progress towards goals. Nineteen semi-structured interviews were conducted with community participants to discuss definitions of success in the HCI and participant experience in developing indicators. Three themes emerged: the formal indicators lacked relevance to community members; the community did not own the HCI indicators and participants instead drew upon measures of success which were largely experiential in nature. The study provides a critically reflective, candid account of on-the-ground work with communities. The findings reveal limitations in the process of developing community indicators in this HCI, which we attribute in part to skills and discontinuities on the staffing side of the health authority and in part to failure to recognize and fully appreciate 'different ways of knowing' between communities and agencies.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".