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Record W2802872978 · doi:10.1177/1557988318768607

Developing Resilience: Gay Men’s Response to Systemic Discrimination

2018· review· en· W2802872978 on OpenAlexafffund
Ingrid Handlovsky, Vicky Bungay, John L. Oliffe, Joy L. Johnson

Bibliographic record

VenueAmerican Journal of Men s Health · 2018
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia Hospital
FundersCanadian Institutes of Health Research
KeywordsPsychological resilienceMental healthPsychologyGrounded theoryAnxietyGerontologySocial psychologyMedicineSociologyPsychiatryQualitative research

Abstract

fetched live from OpenAlex

Gay men experience marked health disparities compared to heterosexual men, associated with profound discrimination. Resilience as a concept has received growing attention to increase understanding about how gay men promote and protect their health in the presence of adversity. Missing in this literature are the perspectives and experiences of gay men over 40 years. This investigation, drawing on grounded theory methods, examined how gay men over 40 years of age develop resilience over the course of their lives to promote and protect their health. In-depth interviews were undertaken with 25 men ranging between 40 and 76 years of age who experienced an array of health concerns including depression, anxiety, suicidality, and HIV. Men actively resist discrimination via three interrelated protective processes that dynamically influence the development of resilience over their life course: (a) building and sustaining networks, (b) addressing mental health, and (c) advocating for respectful care encounters. Initiatives to promote and protect the health of gay men must be rooted in the recognition of the systemic role of discrimination, while supporting men's resilience in actively resisting discrimination.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.107
GPT teacher head0.502
Teacher spread0.395 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2018
Admission routes2
Has abstractyes

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