Movement-Based Programs in U.S. and Canadian Public Libraries: Evidence of Impacts from an Exploratory Survey
Bibliographic record
Abstract
Abstract Objective – Past research suggests that approximately 20-30% of public libraries in the United States offer movement-based programs, that is programs that encourage, enable, or foster physical activity and physical fitness. Little is currently known about the impacts of these programs, in the U.S. or elsewhere. This study addresses the questions: what impacts do movement-based programs in public libraries have and what variations exist between urban and rural libraries. Methods – The researcher aimed to explore these questions through an exploratory survey of U.S. and Canadian public libraries that have offered movement-based programs. The survey was completed by self-selecting staff from 1,157 public libraries in the U.S. and Canada during spring 2017. Analysis focuses on those portions of the survey that address the impacts of movement-based programs. Results – Results show that throughout North America, public libraries provide movement-based programs for all age groups. The most consistently reported impact of these programs is new library users. Furthermore, on average respondents report that participation in these programs slightly exceeding their expectations. These facts may account for the finding that 95% of respondents report that they intend to continue offering movement-based programs at their libraries. Conclusion – More research using a randomized survey design is needed to better assess this emerging programming area in a more comprehensive manner. Nonetheless, this study provides needed evidence on the impacts of movement-based programs in many North American public libraries. Hopefully this evidence will contribute to more conversations and research on the roles of public libraries in public health and wellness.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".