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Record W2984231739 · doi:10.51656/psycause.v8i2.20123

Compassion ou respiration ?

2019· article· fr· W2984231739 on OpenAlexaffvenue
Sophie Aubé, Catherine Bégin, Valérie Leblanc, Sonia Goulet

Bibliographic record

VenuePsycause revue scientifique étudiante de l École de psychologie de l Université Laval · 2019
Typearticle
Languagefr
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

La méditation pour promouvoir la santé psychologique suscite de l’intérêt et il y a lieu d’examiner si l’impact varie selon l’objet de l’attention. Cette étude compare les effets des méditations sur le souffle (MS) et de compassion (MC) sur la compassion, le stress, les affects et la pleine conscience, chez une population non clinique, féminine et universitaire. Seize volontaires assignées aléatoirement entre deux groupes reçoivent comme consignes de pratiquer la MS ou la MC individuellement 20 minutes, six jours par semaine, pendant trois semaines consécutives, au moyen d’instructions écrites. Les mesures pré-test et post-test comprennent un journal de bord et des questionnaires auto-rapportés. Comparativement au Groupe MS, le Groupe MC montre une plus grande augmentation de la compassion et des affects positifs, une diminution plus élevée du stress perçu et des affects négatifs, mais une augmentation moindre de la pleine conscience. Tous ces résultats s’avèrent non significatifs, bien qu’une augmentation significative du niveau de pleine conscience ressorte au deuxième temps de mesure par rapport au niveau de base pour les deux groupes combinés. Ce rapport aborde les limites et contributions de l’étude en regard de la promotion du mieux-être psychologique chez la population d’intérêt au moyen des pratiques méditatives ciblées.

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)0.001
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.004

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 head0.044
GPT teacher head0.316
Teacher spread0.271 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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