MétaCan
Menu
Back to cohort
Record W3030319415 · doi:10.1177/1049732320919094

Older Adults’ Narratives of Seeking Mental Health Treatment: Making Sense of Mental Health Challenges and “Muddling Through” to Care

2020· article· en· W3030319415 on OpenAlexafffund
Kristin Reynolds, Maria I. Medved, Corey S. Mackenzie, Laura Funk, Lesley Koven

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsMental healthNarrativeResistance (ecology)PsychologyMeaning (existential)Narrative inquiryClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Older adults who experience challenges related to mental health are unlikely to seek professional help. The voices of older adults who have navigated through mental health issues and systems of care to arrive at psychological treatment are less well understood. We conducted individual interviews with 15 adults aged 61 to 86 who sought psychological treatment. Interviews were audio-recorded, transcribed, and analyzed using narrative methods. We identified several main storylines that describe the meaning-making and treatment-seeking journeys of older adults: resistance to being labeled with mental health problems (telling stories of resistance, defining mental health issues in mysterious and uncontrollable terms, and experiencing internal role conflict); muddling through the help-seeking process (manifestations of chaos and system-level barriers); and emotional reactions to psychological treatment (hope, fear, and mistrust). Findings add to the literature base in the area of narrative gerontology, and highlight the complex experiences that older adults face when seeking psychological treatment.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.739
GPT teacher head0.646
Teacher spread0.093 · 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

Citations33
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicMental Health and Patient InvolvementFrench-language works237,207