MétaCan
Menu
Back to cohort
Record W4403004383 · doi:10.1080/07317115.2024.2408762

Mental Health Literacy Reduces the Impact of Internalized Stigma on Older Adults' Attitudes and Intentions to Seek Mental Health Services

2024· article· en· W4403004383 on OpenAlexaff
Corey S. Mackenzie, Melissa A. Krook, Dallas J. Murphy, Li-elle Rapaport

Bibliographic record

VenueClinical Gerontologist · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Manitoba
FundersMovember Foundation
KeywordsMental healthMental health literacyStigma (botany)PsychologyClinical psychologyPsychiatryLiteracyGerontologyMental illnessMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Older adults are the least likely age group to seek mental health services, and internalized stigma is an important reason why. We sought to further our understanding of which older adults are particularly likely to be affected by internalized stigma, and why, by investigating mental health literacy (MHL) as a moderator within the internalized stigma model of help-seeking. METHODS: We utilized a conditional process analysis of cross-sectional, secondary data from 350 distressed older adults. Participants completed an online survey consisting of measures of distress, perceived control, experiential avoidance, MHL, public and self-stigma of seeking help, help-seeking attitudes, and conditional help-seeking intentions. RESULTS: MHL moderated the internalized stigma model; distressed older adults with lower MHL were more likely to have public stigma internalized as self-stigma, which then reduced their intentions to seek help. More specifically, low MHL magnified the negative effect of self-stigma on attitudes and intentions. CONCLUSIONS: These results increase our understanding of which older adults are less likely to seek mental health services: distressed older adults with poor MHL and high self-stigma. CLINICAL IMPLICATIONS: MHL is a malleable construct that can be targeted by interventions designed to increase help-seeking among distressed older adults in need of professional help.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.077
GPT teacher head0.539
Teacher spread0.462 · 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 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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueClinical GerontologistSame topicMental Health Treatment and AccessFrench-language works237,207