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Record W2170847042 · doi:10.1177/1049732313509408

Masculinities, Work, and Retirement Among Older Men Who Experience Depression

2013· article· en· W2170847042 on OpenAlexafffundabout
John L. Oliffe, Brian Rasmussen, Joan L. Bottorff, Mary T. Kelly, Paul Galdas, Alison Phinney, John S. Ogrodniczuk

Bibliographic record

VenueQualitative Health Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of British Columbia
FundersMichael Smith Health Research BC
KeywordsDepression (economics)Work (physics)Thematic analysisPsychologyDiversity (politics)CentralityGender studiesQualitative researchGerontologySociologyMedicineSocial science

Abstract

fetched live from OpenAlex

The high incidence of depression among older men has been linked to numerous factors. In this qualitative descriptive study of 30 older, Canadian-based men who experienced depression, we explored the connections between participants' depression, masculinities, work, and retirement. Our analyses revealed three thematic findings. The recursive relationship between depression and work was reflected in depression impeding and emerging from paid work, whereby men's careers and work achievements were negatively impacted by depression amid assertions that unfulfilling work could also invoke depression. Lost or unrealized empires highlighted the centrality of wealth accumulation and negative impact of many participants' unfulfilled paid work aspirations. Retirement as loss and the therapeutic value of work reflected how masculine ideals influenced men to continue working to avoid the losses they associated with retirement. The findings confirm the need to support men's work-related transitions by affirming a diversity of masculine identities beyond traditional workman/breadwinner roles.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.249
Threshold uncertainty score0.495

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.338
GPT teacher head0.546
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations76
Published2013
Admission routes3
Has abstractyes

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