Effectiveness of past and current critical incident analysis on reflective learning and practice change
Notice bibliographique
Résumé
OBJECTIVES: Critical incident analysis (CIA) is one of the strategies frequently used to facilitate reflective learning. It involves the thorough description and analysis of an authentic and experienced event within its specific context. However, CIA has also been described as having the potential to expose vulnerabilities, threaten learners' coping mechanisms and increase rather than reduce their anxiety levels. The aim of this study was to compare the analysis of current critical incidents with that of past critical incidents, and to further explore why and how the former is more conducive to reflective learning and practice change than the latter. METHODS: A collaborative research study was conducted. Eight occupational therapists were recruited to participate in a reflective learning group that convened for 12 meetings held over a 15-month period. The group facilitator planned and adapted the learning strategies to be used to promote reflective learning and guided the group process. Critical incident analysis represented the main activity carried out in the group discussions. The data collected were analysed using the grounded theory method. RESULTS: Three phenomena were found to differentiate between the learning contexts created by the analysis of, respectively, past and current critical incidents: attitudinal disposition; legitimacy of purpose, and the availability of opportunities for experimentation. Analysis of current clinical events was found to improve participants' motivation to self-evaluate, to increase their self-efficacy, and to help them transfer learning into action and to progressively self-regulate. CONCLUSIONS: The results of this collaborative research study suggest that the analysis of current clinical events in order to promote reflection offers a safer and more constructive learning environment than does the analysis of incidents that have occurred in the past. This learning strategy is directly grounded in health professional practice. The remaining challenge for continuing education providers is that of creating conditions conducive to its use.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».