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Record W4390813454 · doi:10.7870/cjcmh-2023-026

Men’s Sheds and Mental Health in Rural Communities: Exploring the Benefits of a Community-Level Program

2023· article· en· W4390813454 on OpenAlexaffvenueabout
Clark Banack, Kyle Whitfield, Serena Isley

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMental healthPsychologyInclusion (mineral)Exploratory researchSet (abstract data type)GerontologyRural areaSociologySocial psychologyMedicinePsychiatrySocial science

Abstract

fetched live from OpenAlex

Rural regions across North America continue to suffer from a lack of community-level mental health supports. This exploratory study explores whether Men’s Sheds, bottom-up, community-driven groups designed to support retired and older men’s mental health by mimicking the social and collaborative aspects of “work-life” by creating opportunities to engage in project-based woodworking, metalworking or mechanics, are generating positive mental health outcomes for their members in rural communities in Alberta, Canada. Relying on a set of semi-structured interviews with participants across two rural Alberta Men’s Sheds, in addition to a sociodemographic and self-rating questionnaire, we demonstrate that the participants in these Sheds enjoy clear and significant mental health benefits by generating opportunities for camaraderie, a sense of purpose and a sense of inclusion. Although not an appropriate substitute for more formal mental health supports in certain situations, we conclude that supporting the formation of new Men’s Sheds throughout rural areas represents a worthwhile investment in the mental health of a group of vulnerable citizens.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.215
GPT teacher head0.377
Teacher spread0.162 · 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 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

Citations2
Published2023
Admission routes3
Has abstractyes

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