MétaCan
Menu
Back to cohort
Record W2094378893 · doi:10.1027/0227-5910/a000259

Shadows From the Past

2014· article· en· W2094378893 on OpenAlexaff
Anne Lise Holm, Anne Lyberg, Ingela Berggren, John R. Cutcliffe, Elisabeth Severinsson

Bibliographic record

VenueCrisis · 2014
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFeelingMeaning (existential)Thematic analysisDistressPsychologyMental healthQualitative researchTheme (computing)WishLived experienceNursingPsychotherapistSocial psychologyMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Most depressed older people in a suicidal state have mixed feelings, where the wish to live and the wish to die wage a battle. AIMS: To explore and describe depressed older people's experiences of being suicidal and their search for meaning. METHOD: Data were collected from 29 participants resident in the Rogaland and Vestfold districts of Norway, by means of individual interviews, after which a thematic analysis was performed. RESULTS: For the participants in this study, the lived experiences of the situated meaning of survival after being suicidal comprised a main theme - "shadows from the past" - and two themes - "feeling that something inside is broken" and "a struggle to catch the light." CONCLUSION: Mental health-care professionals might be able to reduce the risk of suicide and perturbation by helping depressed older people to explore, resolve, and ultimately come to terms with their unresolved historical issues. Additional valuable strategies in primary care settings include encountering patients frequently, monitoring adherence to care plans, and providing support to address the source of emotional pain and distress.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.014
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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 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

Citations16
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCrisisSame topicSuicide and Self-Harm StudiesFrench-language works237,207