Public Libraries and Health Promotion Partnerships: Needs and Opportunities
Bibliographic record
Abstract
Objective – Across North America, public libraries have increasingly served their communities by working with partners to connect patrons to essential healthcare services, including preventative. However, little is known about the extent of these partnerships, or the need for them, as seen from the perspective of public library workers. In this study, we set out to address the following research question: What needs and opportunities are associated with health promotion partnerships involving public libraries? Methods – Using snowball sampling techniques, in September 2021, 123 library workers from across the state of South Carolina in the United States (US) completed an online survey about their health partnerships and health-related continuing education needs; an additional 19 completed a portion of the survey. Results – Key findings included that library capacity is limited, but the desire to support health via partnerships is strong. There is a need for health partnerships to increase library capacity to support health. Public libraries already offer a range of health-related services. Finally, disparities exist across regions and between urban and rural communities. Conclusion – As an exploratory study based on a self-selecting sample of public library workers in a particular state of the US, this study has some limitations. Nonetheless, this article highlights implications for a variety of stakeholder groups, including library workers and administrators, funders, and policy makers, and researchers. For researchers, the primary implication is the need to better understand, both from the public library worker’s perspective and from the (actual or potential) health partner’s perspective, needs and opportunities associated with this form of partnership work.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".