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Record W4283518614 · doi:10.1177/10497323221110974

Mapping Men’s Mental Health Help-Seeking After an Intimate Partner Relationship Break-Up

2022· article· en· W4283518614 on OpenAlexafffund
John L. Oliffe, Mary T. Kelly, Gabriela Gonzalez Montaner, Zac E. Seidler, David Kealy, John S. Ogrodniczuk, Simon Rice

Bibliographic record

VenueQualitative Health Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of British Columbia
FundersNational Health and Medical Research CouncilCanada Research ChairsUniversity of MelbourneMovember Foundation
KeywordsMental healthPsychologyPromotion (chess)Help-seekingHealth promotionSocial psychologyNursingPsychiatryMedicinePublic health

Abstract

fetched live from OpenAlex

Deleterious effects of separation and divorce on men's mental health are well-documented; however, little is known about their help-seeking when adjusting to these all-too-common life transitions. Employing interpretive descriptive methods, interviews with 47 men exploring their mental health help-seeking after a relationship break-up were analyzed in deriving three themes: (1) Solitary work and tapping established connections, (2) Reaching out to make new connections, and (3) Engaging professional mental health care. Men relying on solitary work and established connections accessed relationship-focused self-help books, online resources, and confided in friends and/or family. Some participants supplemented solitary work by reaching out to make new connections including peer-based men's groups and education and social activities. Comprising first-time, returning, and continuing users, many men responded to relationship break-up crises by engaging professional mental health care. The findings challenge longstanding commentaries that men actively avoid mental health promotion by illuminating wide-ranging help resources.

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)0.035
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.524
GPT teacher head0.602
Teacher spread0.078 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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