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Record W3030272896 · doi:10.1097/nmd.0000000000001153

Distress Concealment and Depression Symptoms in a National Sample of Canadian Men

2020· article· en· W3030272896 on OpenAlexaffabout
Daniel W. Cox, John S. Ogrodniczuk, John L. Oliffe, David Kealy, Simon Rice, Jeffrey H. Kahn

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLonelinessDistressFeelingPsychologySocial connectednessInterpersonal communicationMediationDepression (economics)Clinical psychologyAssociation (psychology)Depressive symptomsPopulationPsychiatryMedicineSocial psychologyAnxietyPsychotherapist

Abstract

fetched live from OpenAlex

Men's tendency to conceal their distress has been linked with increased depressive symptoms. Although interpersonal connectedness has been associated with distress concealment and depression, it is unclear how connectedness mediates this association. The aim of the present study was to examine the mediating effects of feeling understood and loneliness-two facets of interpersonal connectedness-in the association between distress concealment and depressive symptoms in men. A sample of 530 Canadian men was selected based on age- and region-stratification that reflects the national population. Participants completed measures of depression symptoms, distress concealment, loneliness, and feeling understood. Mediation analyses were conducted. Results supported a sequential mediation model: concealing distress was associated with not feeling understood, not feeling understood was associated with loneliness, and loneliness was associated with depressive symptoms. These findings shed light on how distress concealment is associated with depressive symptoms among men. Implications for practice and theory 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 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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 designObservational
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

Citations21
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Nervous and Mental DiseaseSame topicChild Abuse and TraumaFrench-language works237,207