Social Prescribing in a Japanese Community Library Shows Positive Impacts on Participants
Bibliographic record
Abstract
A Review of: Morimoto, Y., Koga, Y., Kenzaka, T., & Son, D. (2023). Social prescribing initiative at community library and its impact on residents and the community: A qualitative study. Journal of Primary Care & Community Health, 14, https://doi.org/10.1177/21501319231181877 Objective – To determine the impacts of social prescribing in community libraries when medical and social professionals participate. Design – Qualitative study using semi-structured interviews Setting – A community library operated by medical and social support staff in Toyooka City, Japan. Subjects – 10 library participants of various ages, genders, occupations, and levels of involvement. Methods – Two of the authors in this study conducted semi-structured interviews with the users, volunteers, and staff of a community library to solicit their experiences in participating in this initiative. Using an interview guide, data was collected from study participants at the community library site or at a local college, and interviews took place in Japanese, but data was later translated to English post-transcription and analysis. This analysis was completed using the Steps for Coding and Theorization method (or SCAT) (Otani, 2008) for qualitative analysis. Main Results – The authors’ analysis of interview data revealed 11 major categories that participants spoke of the community library offering them, such as “a place to stay, attractive space design, diverse accessibility, choosability of various roles, consultation function, social support, empowerment, mutual trust, formation of connections across generations/attributes, co-creation, and social impact”. Conclusion – Embedding primary care medical providers and staff who recommend or provide social supports in community libraries can reduce barriers to access in both domains and improve the local community overall. This study has implications for all libraries that welcome users to partake in supplemental services and events. However, public libraries should take special note of this study’s findings as they could be inspired to incorporate community members, primary care providers, and social supports into their service provision.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".