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Record W1974571293 · doi:10.1027/0227-5910.28.1.16

Addressing Suicidal Ideations Through the Realization of Meaningful Personal Goals

2007· article· en· W1974571293 on OpenAlexaff
Sylvie Lapierre, Micheline Dubé, Léandre Bouffard, Michel Alain

Bibliographic record

VenueCrisis · 2007
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychologyFlexibility (engineering)Psychological distressSet (abstract data type)Depression (economics)Intervention (counseling)Realization (probability)Clinical psychologyControl (management)DistressPlan (archaeology)Sample (material)Applied psychologySocial psychologyPsychotherapistMental healthPsychiatryComputer science

Abstract

fetched live from OpenAlex

A personal goal intervention program was offered to early retirees aged 50 to 65 years with the objective of increasing their subjective well-being. The program was aimed at helping the participants set, plan, pursue, and realize their personal goals. A subsample of 21 participants with suicidal ideas was identified from a larger sample (N = 354) of retirees living in the community who took part in the study to evaluate the program. The experimental (n = 10) and control (n = 11) groups were compared on their answers to 16 goal and psychological well-being questionnaires. By the end of the program, the experimental group had improved significantly more than the control group on hope, goal realization process, serenity, flexibility, and positive attitude toward retirement. The levels of depression and psychological distress significantly decreased. These gains were maintained 6 months later. The positive results obtained from this study could lead to an innovative way to help people with suicidal ideations.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.124
GPT teacher head0.473
Teacher spread0.350 · 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

Citations95
Published2007
Admission routes1
Has abstractyes

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