Examining the impact and motivations of play in academic libraries: A cross-sectional study
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Introduction: Academic libraries are well situated to provide students with opportunities to de-stress through play. However, the impacts of play and student motivations to participate have not been formally investigated. This study aimed to answer the questions: What are the impacts of play in academic libraries? And why do students choose to participate in play events in these settings? Methods: A cross-sectional study design was used. Recruitment took place at three academic libraries in Canada. Participants were University students actively engaging in a play event. An eight-item questionnaire was distributed. Results were analyzed using descriptive statistics and thematic analysis. Results: 93 participants completed the study. The majority were undergraduate, domestic students residing off-campus. The impact of personal enjoyment had the highest mean response among the impacts measured. The top motivations for participating were to take a break, to engage in the activity being offered, and for enjoyment or fun. Discussion: Participants were motivated and impacted by personal enjoyment, aligning with previous research on play in adulthood. However, the need to take a break was the strongest motivator to participate in play events for this post-secondary student population. Academic libraries are an optimal place on campus for play events to occur.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".