Bibliographic record
Abstract
<div><p>Background</p><p>Understanding what promotes or hinders a community’s capacity to serve the priorities of its residents is essential for the alignment of citizen needs and governance. Participatory approaches that engage community residents on the topic of community wellbeing are useful methods for defining outcomes that reflect a community’s goals and priorities. Using qualitative focus group methods, the aim of this study was to outline bottom-up definitions of community wellbeing from a diverse pool of community residents in Ontario, Canada.</p><p>Methods</p><p>Semi-structured, two-hour group interviews were conducted with adult (≥18 years) participants (N = 15) residing in four communities across Canada’s largest province of Ontario. Participants were purposively selected from a pool of screening questionnaires to ensure diverse group compositions based on race, gender, age, and educational attainment. Interviews were thematically analysed using descriptive and interpretive methods to characterize resident conceptions of community wellbeing.</p><p>Results</p><p>Focus group participants were between 18 and 75 years of age and most had lived in their local community for 5 or more years. Four major themes emerged: (1) a sense of community belonging is cultivated through shared spaces, routines, support, and identities; (2) a community constitutes the amenities and social contexts that enable residents to thrive; (3) effective regional decision-making must be community-informed; and (4) the wellbeing of a community relies on equal opportunities for engagement and participation.</p><p>Conclusions</p><p>Residents described their communities and their associated wellbeing as a combination of accessible amenities and opportunities to engage without marginalization. This study underscores the value of participatory approaches in community wellbeing research, where the viewpoint and life experience of residents is used to inform local decision-making and service delivery. Future research will capture more diverse perspectives towards community belonging, particularly from community newcomers, for the development of regionally appropriate indicators of community wellbeing.</p></div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.962 | 0.084 |
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; both teacher heads agree on what is shown here.
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".