Interventions to improve question formulation in professional practice and self-directed learning
Notice bibliographique
Résumé
BACKGROUND: Formulating questions is fundamental to the daily life of a healthcare worker and is a defining characteristic of professional competence and meaningful learning. With high expectations for healthcare providers to remain up-to-date with current evidence and the movement towards formalizing reflective practice as part of physician revalidation, it is important that curricula developed for improving the ability to formulate well-constructed questions are based on the best evidence. OBJECTIVES: To assess the effectiveness of interventions for increasing the frequency and quality of questions formulated by healthcare providers in practice and the context of self-directed learning. SEARCH STRATEGY: We obtained studies from searches of electronic bibliographic databases, and supplemented these with handsearching, checking reference lists, and consultation with experts. SELECTION CRITERIA: We considered published and unpublished randomized controlled trials (RCTs), controlled clinical trials (CCTs), interrupted time-series (ITS), and controlled before-after (CBA) studies of any language examining interventions for increasing the quality and frequency of questions formulated by health professionals involved with direct patient care. DATA COLLECTION AND ANALYSIS: Two review authors independently undertook all relevancy screening and 'Risk of bias' assessment in duplicate. Intervention characteristics, follow-up intervals, and measurement outcomes were diverse and precluded quantitative analysis. We have summarized data descriptively. MAIN RESULTS: Searches identified four studies examining interventions to improve question formulation in healthcare professionals. Interventions were mostly multi-component, limited within the context of EBM and primarily in physician and resident populations. We did not identify studies examining changes in frequency of questions formulated or those within the context of reflection. Risk of bias was generally rated to be 'high risk'. Three of the four studies showed improvements in question formulation in physicians, residents, or mixed allied health populations in the short- to moderate term follow up. Only one study examined sustainability of effects at one year and reported that skills had eroded over time. AUTHORS' CONCLUSIONS: Evidence from our review suggests that interventions to increase the quality of questions formulated in practice produce mixed results at both short- (immediately following intervention), and moderate-term follow up (up to nine months), comparatively. Although three studies reported effectiveness estimates of an educational intervention for increasing the quality of question formulation within the short term, only one study examined the effectiveness in the longer term (one year) and revealed that search skills had eroded over time. Data suggests that sustainability of effects from educational interventions for question formulation are unknown.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,057 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,008 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».