Library as a Therapeutic Landscape Promoting Health and Well-Being to Chiropractic Students: A Descriptive Report
Bibliographic record
Abstract
Objective: The purpose of this paper is to describe a bibliotherapy-based wellness initiative that was developed at the health science library at Canadian Memorial Chiropractic College. Methods: A comprehensive literature review and consultations with stakeholders were completed. A bibliotherapy program, which included the practice of using books and reading to promote mental health, well-being, and resilience for chiropractic students, was developed and launched in January 2020. The program included shared reading, reflection, and a guided group discussion. Short readings of various genres (i.e., poetry, fiction, nonfiction) were tailored specifically to address psychological, emotional, and social challenges facing students. Results: The program participant feedback showed that shared reading helped students cope with anxiety, worries, and loneliness and isolation caused by the pandemic, as consistent with bibliotherapy research. Conclusion: The bibliotherapy program at this 1 location seemed to be well-received by chiropractic students. The program was recognized by faculty and college administrators as a valuable addition to other mental health and wellness support services available on campus. These findings suggest future research to evaluate the potential efficacy of bibliotherapy for the mental health, well-being, and resilience of chiropractic students.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".