« Comment en parler? » : Création d’une simulation virtuelle sur les soins de santé sexuelle destinés aux survivantes du cancer du sein
Bibliographic record
Abstract
La simulation virtuelle (SV) est une stratégie innovante et proactive d’application pratique de la théorie qui peut améliorer les connaissances et le savoir-faire des professionnels de la santé. Toutefois, aucun article n’a été publié à ce jour sur l’utilisation de la simulation pour améliorer les soins aux survivants du cancer. Le présent article décrit notre expérience de création d’une SV pour former les professionnels de la santé aux problèmes sexuels vécus par les femmes traitées pour un cancer du sein. Si on extrapole à partir des recherches effectuées dans d’autres contextes, la SV permettrait de bonifier les connaissances et compétences dont les professionnels ont besoin pour accompagner les survivantes ayant des préoccupations de nature sexuelle. Notre expérience de développement de SV pourra motiver et guider d’autres chercheurs qui souhaitent eux aussi déployer des interventions similaires. Mots-clés : innovation infirmière, jeu de simulation virtuelle, survivants du cancer, soins primaires
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".