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Record W2669960407 · doi:10.7202/1040075ar

Un programme innovateur de promotion du bien-être psychologique POUR des personnes Âgées dépressives

2017· article· fr· W2669960407 on OpenAlexaffvenue
Sylvie Lapierre, Lyson Marcoux, Sophie Desjardins, Micheline Dubé, Michael Cantinotti, Paule Miquelon, Richard Boyer, Michel Alain, Marjorie Duchesne-Beauchamp

Bibliographic record

VenueRevue québécoise de psychologie · 2017
Typearticle
Languagefr
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsInstitut universitaire en santé mentale de MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La recherche dans le domaine de la motivation a démontré que la présence de buts personnels est associée au bien-être psychologique (BEP). Un programme de 14 semaines conçu pour aider les participants à réaliser leurs projets a été offert à des personnes âgées (≥ 65 ans) dépressives (BDI-II ≥ 9; M = 22,05) afin d’améliorer leur BEP. Les niveaux de BEP, de dépression et d’anxiété des personnes qui ont participé au programme (n = 24) ont été comparés à ceux d’un groupe contrôle (n = 18). Les analyses ont montré que les participants au programme se sont améliorés significativement sur la plupart des indicateurs de BEP, incluant la dépression, ce qui semble indiquer qu’un programme de gestion des buts pourrait être une manière innovatrice de promouvoir la santé mentale des aînés dépressifs. Toutefois, l’amélioration est observée uniquement pour la sérénité et le sens à la vie dans les analyses comparatives avec le groupe contrôle. Les études ultérieures devraient tenter de développer des programmes de promotion de la santé mentale qui offrent aux individus des moyens d’atteindre un état de fonctionnement optimal.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.075
GPT teacher head0.357
Teacher spread0.282 · 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 designNot applicable
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

Citations1
Published2017
Admission routes2
Has abstractyes

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