Bibliographic record
Abstract
The benefits of physical activity (PA) are well recognized. While the benefits of PA contribute to a higher quality of life, physical inactivity remains a population-wide plague around the world. The health risks associated with inactivity are disproportionately high among those with low-incomes. While the barriers hindering an active lifestyle among low-income persons are well known, there is a paucity of literature on the perspectives of those that are providing recreation services to low-income citizens. It is anticipated that understanding the professionals’ perspectives may help refine municipal recreation policies and procedures in order to better reach and serve low-income citizens. This study sought to understand the benefits of and challenges to implementing programs designed for low-income citizens. Framed by social marketing theory, key informant interviews and a focus group were conducted with recreation professionals from Vancouver Island, British Columbia, Canada ( N = 9). Social marketing can be a powerful way to make sense of how policies and programs are positioned by recreation professionals and offered to their less well-resourced patrons. Implications for practice include delivering programs outside of facility walls, using word of mouth as a trusted communication method, developing subsidy policies, seeking external funding sources, and relying on partnerships to facilitate delivery and recruitment. By focusing on the recreation professional, this study reveals the importance of targeting upstream audiences in order to effect change for the end users. Future research in other social service professions might benefit from a similar approach to advancing service delivery.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.225 | 0.032 |
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".