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Record W4212935259 · doi:10.3928/02793695-20220215-03

Effects of Developmental Bibliotherapy on Subjective Well-Being of Older Adults Living in Nursing Homes: A Quasi-Experimental Study

2022· article· en· W4212935259 on OpenAlexaboutno aff
Hongxia Zhang, Zhen Chen, Jinhua Zhang, Xiaoyan Zhou, Suqing Li, Hongmei Ren

Bibliographic record

VenueJournal of Psychosocial Nursing and Mental Health Services · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsBibliotherapyOptimismPsychosocialHappinessPsychologyIntervention (counseling)Mental healthGeriatric Depression ScaleWell-beingGerontologyLife satisfactionScale (ratio)Clinical psychologyMedicinePsychiatryCognitionPsychotherapist

Abstract

fetched live from OpenAlex

The current study sought to create a developmental bibliotherapy material database (DBMD) and examine the effectiveness of developmental bibliotherapy on subjective well-being of older adults living in nursing homes. Based on the reading needs of older adults, we developed a DBMD, which included 327 materials with five themes: Health Care , Current Affairs and Politics , Historical Biographies , Geriatric Culture , and Psychological Adjustment . Fifty-four single materials were randomly selected from the DBMD to perform the intervention. This study used a quasi-experimental, single-group pre-/post-survey approach. Sixty-four older adults participated in the study for 6 weeks. Immediately before and after the intervention, older adults completed the Optimism-Pessimism Scale and Memorial University of Newfoundland Scale of Happiness. There were significant improvements in older adults' optimistic tendency and subjective well-being ( p < 0.05). Reading materials in the DBMD promoted older adults' optimistic attitude toward life, reduced negative emotions, and improved subjective well-being. [ Journal of Psychosocial Nursing and Mental Health Services, 60 (7), 15–22.]

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.381
Teacher spread0.372 · 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 designQualitative
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

Citations5
Published2022
Admission routes1
Has abstractyes

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