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Record W4399780185 · doi:10.1177/15579883241260920

Understanding Men’s Lived Experience of Mental Distress Through Metaphors

2024· article· en· W4399780185 on OpenAlexafffund
Sarah McKenzie, Fiona Mathieson, Tiara Das, Matthew C. Genuchi, John L. Oliffe

Bibliographic record

VenueAmerican Journal of Men s Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsUniversity of British Columbia
FundersMarsden FundCanada Research Chairs
KeywordsMetaphorDistressMental healthPsychologyMeaning (existential)Mental distressQualitative researchPhotovoicePerspective (graphical)Social psychologyPsychotherapistSociologyLinguisticsSocial science

Abstract

fetched live from OpenAlex

The use of tailored language, which involves a clinician’s ability to adapt communication styles and employ accessible terms and concepts, has long been touted as key to engaging men with mental health services. Metaphors are one communication device that can provide men with ways through which to meaningfully express themselves and communicate their mental distress experiences. Using qualitative photovoice research, the current study examined how New Zealand-based men ( n = 21) communicatively constructed their meaning of mental distress through metaphors. Analysis of interview data was used to derive three metaphor groupings men consistently drew on to articulate their lived experiences: metaphors of emotions ( darkness and weight), metaphors of survival ( battle and entity), and metaphors of disembodiments ( debility and entrapment). The findings highlight the power of metaphors as a tool for men in communicating their experiences of mental distress and are valuable for health professionals to contemplate across an array of contexts. The implications and importance of a metaphor-enriched perspective for engaging men in professional health care settings and services are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.697

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 head0.223
GPT teacher head0.505
Teacher spread0.282 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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