A Web-Based Therapist Training Tutorial on Prolonged Grief Disorder Therapy: Pre-Post Assessment Study
Notice bibliographique
Résumé
Background Prolonged grief disorder (PGD) is a newly recognized mental disorder characterized by pervasive intense grief that persists longer than cultural or social expectations and interferes with functioning. The COVID-19 epidemic has resulted in increased rates of PGD, and few clinicians feel confident in treating this condition. PGD therapy (PGDT) is a simple, short-term, and evidence-based treatment developed in tandem with the validation of the PGD diagnosis. To facilitate the dissemination of PGDT training, we developed a web-based therapist tutorial that includes didactic training on PGDT concepts and principles as well as web-based multimedia patient scenarios and examples of clinical implementation of PGDT. Objective We aimed to evaluate user satisfaction with the tutorial and whether the tutorial increased trainees’ knowledge of PGDT principles and procedures. Moreover, we included a small number of pilot questions to evaluate the PGDT-related clinical skills. Methods This study evaluated tutorial learning using a pre- and poststudy design. Participants were recruited from professional organization mailing lists, announcements to graduates of the Columbia School of Social Work, and through word of mouth. After signing consent, participants completed a brief demographic survey, a 55-item multiple-choice prestudy test on the concepts and principles of PGD and PGDT covered in the tutorial, and a 4-item pilot web-based prestudy test to gauge PGD clinical implementation skills. The link to the course content was then activated, and participants were given 8 weeks to complete the 11-module tutorial containing information, web-based exercises, simulated patient and video examples, and self-tests. Results Overall, 406 clinicians signed consent, and 236 (58.1%) started the tutorial. Of these, 83.1% (196/236) completed all 11 modules. Trainee scores on our PDGT assessment improved substantially from pretraining to the postmodule assessment, with the total number of correct answers increasing from a mean of 29 (SD 5.5; 52.7% correct) to 36.7 (SD 5.2; 66.7% correct; t195=18.93; P<.001). In addition, the trainee’s implementation scores on 4 clinical vignettes increased from 2.6 (SD 0.7) correct out of 4 to 3.1 (SD 0.4) out of 4 (t188=7.02; P<.001). Effect sizes (Cohen d) were 1.44 (95% CI 1.23-1.65) for PDGT assessment and 1.06 (95% CI 0.84-1.29) for implementation. Trainees found the tutorial interesting, enjoyable, clearly presented, and useful for professional development. They endorsed a mean score of 3.7 (SD 0.47) on a 1 to 4 scale of agreement with recommending the course to others and feeling satisfied with the tutorial, and a mean of 3.3 (SD 0.57) with feeling able to apply the skills with clients. Conclusions This pilot study provides support for the usefulness of this web-based training for teaching clinicians how to administer PGDT. The addition of patient scenarios for clinical implementation strategies holds promise for increasing the effectiveness of PGDT training and other evidence-based treatments. Trial Registration ClinicalTrials.gov NCT05121792; https://www.clinicaltrials.gov/ct2/show/NCT05121792
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 | 0,001 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».