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Record W2137346025 · doi:10.1177/0034355213486359

Leisure-Generated Meanings and Active Living for Persons With Mental Illness

2013· article· en· W2137346025 on OpenAlexaff
Yoshitaka Iwasaki, Catherine Coyle, John Shank, Emily Messina, Heather R. Porter

Bibliographic record

VenueRehabilitation Counseling Bulletin · 2013
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMental illnessBoredomPsychologyCoping (psychology)Mental healthRehabilitationPsychiatryClinical psychologyPsychiatric rehabilitationSchizophrenia (object-oriented programming)Bipolar disorderMoodPsychotherapist

Abstract

fetched live from OpenAlex

Leisure may potentially play a key role in rehabilitation counseling, including psychiatric rehabilitation. Based on recovery and positive psychology frameworks in which meaning-making is a central concept, this study examined the role of leisure-generated meanings (LGMs) experienced by culturally diverse individuals with mental illness in potentially helping them better cope with stress, adjust to and recover from mental illness, as well as feel more actively engaged in life. One-on-one survey interviews were conducted with African ( n = 35), Hispanic/Latino ( n = 28), Caucasian ( n = 28), and Asian ( n = 8) American adults (aged between 23 and 78) (total n = 101) with mental illness (e.g., bipolar disorder, n = 32; major depression, n = 23; schizophrenia, n = 22) in Philadelphia, Pennsylvania. Using general linear modeling, we found that LGMs significantly predicted the adjustment to and recovery from mental illness, leisure stress-coping, leisure satisfaction, and perceived active living positively, and lower leisure boredom. The findings have implications for psychiatric rehabilitation to better support persons with mental illness from a strengths-based, meaning-centered, and active-living promotion perspective in which leisure seems to play an important role.

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 score0.253
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
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.259
Teacher spread0.250 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

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