Favoriser la réalisation des activités par le biais de la pleine conscience
Bibliographic record
Abstract
Contexte : depuis plusieurs annees, la meditation de pleine conscience fait l’objet d’un nombre croissant d’etudes a son sujet. Parmi elles, certaines portent sur le lien entre la pleine conscience et les sciences de l’occupation, la pleine conscience semble notamment favoriser la realisation d’activites. 0bjectif : cette etude tente de repondre a la question : « La pleine conscience peut-elle influencer positivement la performance occupationnelle et l’engagement occupationnel d’une personne lors de la realisation d’activites signifiantes ? », l’hypothese posee est : « La pleine conscience augmente la performance et l’engagement occupationnel ». Methode : a partir d’une methode experimentale, un protocole de recherche est monte pour tenter de repondre a la problematique de depart, la Mesure Canadienne du Rendement Occupationnel est utilisee et un questionnaire sur l’engagement occupationnel est construit. Resultats : le protocole devra etre teste sur le terrain. Conclusion : les resultats de ce protocole serviront a comparer les donnees recoltees sur deux groupes experimentaux ayant beneficie ou non de la pleine conscience, ces resultats devraient venir confirmer ou infirmer l’hypothese de recherche.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".