Medical Student Experiences of Engaging in a Psychological Flexibility Skill Training App for Burnout and Well-being: Pilot Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Medical students are at higher risk of burnout than the general population. Interventions that facilitate adaptive coping behaviors (eg, Psychological Flexibility) in the context of inherent stressors associated with medical training could mitigate burnout risk and improve well-being. Delivering these interventions using smartphone apps offers advantages such as accessibility, scalability, mitigation of time and stigma barriers, and facilitation of individual tailoring (individualization). There is a need for feasibility trials with medical students in this emerging field. Formal evaluations of user experiences of app-based psychological skill training are required to identify barriers to and facilitators of engagement and optimize intervention development before implementation in efficacy trials and real-world settings. OBJECTIVE: This study aimed to assess the feasibility of delivering an individualized Psychological Flexibility skill training intervention (Acceptance and Commitment Training [ACTraining]) to medical students using an app-based delivery format. We further aimed to explore how formal evaluation of user experiences might inform and guide the development of this app before implementation in an efficacy trial and future research involving app-delivered psychological skill training for medical students. METHODS: This single-arm study was an early-phase feasibility trial of a stand-alone ACTraining app conducted with a sample of Australian medical students (n=11). We collected app usability and user experience data across a broad range of domains (eg, perceived helpfulness and relevance, learning experiences, and self-efficacy) using self-report questionnaires (quantitative and qualitative) and behavioral engagement outcomes. RESULTS: Behavioral engagement data demonstrated that the app delivered the assessment procedures and individualized ACTraining intervention to medical students as intended. The subjective feedback provided by students who actively engaged with the app was generally positive across several indicators, including usability, perceived relevance and helpfulness, accessibility, maintenance of privacy, and opportunity for self-reflection. Disengagement from the app was an identified challenge throughout the trial. Participant feedback identified several factors that may have affected engagement, such as time, expectations regarding app interface functioning, and individual differences in confidence and self-efficacy when implementing skills. CONCLUSIONS: This study reports user experience data that have been largely absent from the literature on digital psychological interventions for medical students. Our findings demonstrate the preliminary feasibility of an app-delivered ACTraining intervention for medical student well-being and burnout and support the value of future assessment of the efficacy of this approach with larger samples. We consider subjective feedback from medical students in relation to observed engagement and propose how this information might be used to inform the development of this app and future research in this nascent field.
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 | 0,007 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».