The development of measures of community capacity for community-based funding programs in Canada
Bibliographic record
Abstract
Improving community capacity for influencing actions on the determinants of health is an immediate outcome of many Public Health Agency of Canada-funding community-based programs. Despite the importance of this outcome, it has been difficult to measure and describe the contribution of funding programs to improving community capacity. This paper reports on a study conducted to develop and establish the psychometric properties of scales that measure community capacity to address health issues in the context of federally funded community-based programs. A literature review and national think tank with 21 experts informed the development of the first draft of the scales that outlined nine key domains of community capacity. Two focus groups with community practitioners provided information on the face and content validity and general usability of this draft instrument. The revised instrument was sent for pilot testing to 114 community organizations. Qualitative and quantitative analyses were performed to assess the validity, reliability and usability of the instrument. Twenty-nine organizations returned a completed instrument (25% response rate). Principal Component Analysis confirmed scale unidimensionality for eight multi-item scales: all of the component loadings were considered good with all scales loading between 0.60 and 0.92. Scale internal consistency was also considered high with alphas between 0.72 and 0.86 for six of these eight scales. Spearman's correlations were significant for the remaining two multi-item scales (composed of two items each), indicating that the two items for each scale were significantly correlated to each other. One scale could not be analyzed quantitatively, as it contained only a single item. Triangulation of qualitative and quantitative results found consistency in interpretations of scale response sets. Feedback on the instrument indicated interest in using it for project planning and evaluation. Psychometric analyses and triangulation provided evidence of the construct validity and reliability of the instrument. The final instrument covers 9 domains and has a total of 26 items, each with a four-point rating scale and a section for qualitative contextual comments. The instrument provides quantitative and qualitative information on community capacity within the context and scope of community-based funding programs.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".