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Record W2509532161 · doi:10.1188/16.onf.588-594

Acceptability of Bibliotherapy for Patients With Cancer: A Qualitative, Descriptive Study

2016· article· en· W2509532161 on OpenAlexafffundabout
Nicole Roberts, Virginia Lee, Bethsheba Ananng, Annett Körner

Bibliographic record

VenueOncology nursing forum · 2016
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsWorkbookBibliotherapyMedicineCoping (psychology)PsychosocialQualitative researchClinical psychologyFamily medicinePsychotherapistNursingPsychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE/OBJECTIVES: To determine the acceptability of a self-help workbook, Mastering the Art of Coping in Good Times and Bad, for patients with cancer. . RESEARCH APPROACH: Descriptive, qualitative. . SETTING: Participants were recruited from the psychosocial support cancer centers of two tertiary care teaching hospitals in Montreal, Quebec, Canada. . PARTICIPANTS: 18 individuals diagnosed with cancer. . METHODOLOGIC APPROACH: A semistructured interview guide with open-ended questions was used to gather feedback from participants about the workbook. . FINDINGS: 18 participants completed the interviews from which the data emerged. Two main categories were identified from the respondents' interviews regarding the acceptability of the workbook. The first category focuses on content, whereas the other focuses on recommendations. Interviewees specified the following content as most helpful. CONCLUSIONS: Bibliotherapy gives patients access to knowledge to help them cope and engage in their own self-management. The workbook Mastering the Art of Coping in Good Times and Bad may be an acceptable means of helping them manage their stress. . INTERPRETATION: Bibliotherapy is not only cost-effective and easy to administer but also an acceptable minimal intervention.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.496
Teacher spread0.429 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations13
Published2016
Admission routes3
Has abstractyes

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