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Record W4402972707 · doi:10.1016/j.echu.2024.08.001

Library as a Therapeutic Landscape Promoting Health and Well-Being to Chiropractic Students: A Descriptive Report

2024· article· en· W4402972707 on OpenAlexaffabout
Natalia Tukhareli

Bibliographic record

VenueJournal of Chiropractic Humanities · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsChiropracticMedicineDescriptive researchAlternative medicineFamily medicineMedical educationSociologyPathologySocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.056
GPT teacher head0.400
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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