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Record W4320506434 · doi:10.2196/44246

A Web-Based Therapist Training Tutorial on Prolonged Grief Disorder Therapy: Pre-Post Assessment Study

2023· article· en· W4320506434 on OpenAlexvenueno aff
Kenneth A. Kobak, M. Katherine Shear, Natalia Skritskaya, Colleen Gribbin Bloom, Gaelle Bottex

Bibliographic record

VenueJMIR Medical Education · 2023
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
FundersNational Institutes of HealthNational Institute of Mental HealthSmall Business Technology Transfer
KeywordsGriefTest (biology)PsychologyMedical educationMedicinePsychotherapist

Abstract

fetched live from OpenAlex

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: =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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.037
GPT teacher head0.439
Teacher spread0.401 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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