Assessing quality of life for clients of Langs Farm Village Association: A case for inclusion of the social determinants of health approach in addressing quality of life in a community health centre setting
Bibliographic record
Abstract
In my master’s thesis research I investigated the relationship between participant inclusion in Langs Farm Village Association, a local community health centre, and quality of life. A quality of life survey which I developed based on prior research conducted by the Canadian Policy Research Network (2001) was administered to 130 individuals, 65 participants who attended a program and/or service at Langs (Langs group), and 65 participants who did not (non-Langs group). Results of multivariate ANOVA indicated group differences of statistical significance on four out of eleven subscales of my quality of life survey. Community residents who participated in a program or service at Langs Farm Village Association reported higher quality of life on three out of eleven survey subscales. I have described and explained factors associated with quality of life for Langs’ participants related to these survey results. In concluded that quality of life can be improved for community residents (especially those who are most vulnerable or at-risk) who access programs and/or services at Langs. My findings are discussed in terms of addressing the social determinants of health through a community health centre setting in order to improve quality of life for its participants and patients.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".