Social Prescribing and Therapeutic Recreation: Making the Connection
Bibliographic record
Abstract
An increasing number of people are experiencing social isolation and loneliness and this trend is becoming cause for concern around the world. Considering that isolation and loneliness give rise to a number of health problems, it is essential to find innovative ways to address this issue. One such approach is to enhance experiences of belonging within communities. Social prescribing (SP) is a method that can promote belonging by connecting people to the social support they need. The purpose of this paper is to explore the potential relationship that can exist between therapeutic recreation (TR) and SP. As we explain, TR can complement SP efforts by ensuring people have access to inclusive, social leisure and recreation opportunities. In this sense, TR professionals are well positioned to be key players in SP processes. We contend that TR practice is best positioned to work in tangent with SP to nurture socially connected communities when it focuses on building community capacity, facilitates welcoming and inclusive leisure and recreation experiences that foster regular social interaction, and adopts principles of community development as part of a social justice model of practice.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".