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Record W4377011023 · doi:10.1177/13623613231173758

Evaluation of an adapted virtual training for master trainers of the WHO Caregiver Skills Training Program during the COVID-19 pandemic

2023· article· en· W4377011023 on OpenAlexafffund
Alaa T. Ibrahim, Afiqah Yusuf, Hannah Pickard, Pamela Dixon, Andy Shih, Stephanie Shire, Andrew Pickles, Mayada Elsabbagh

Bibliographic record

VenueAutism · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéPublic Health AgencyPublic Health Agency of CanadaCanadian Institutes of Health ResearchAutism Speaks
KeywordsPsychologyMedical educationPsychological interventionPandemicTraining (meteorology)Professional developmentCoding (social sciences)NursingCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

Significant barriers to training have been introduced by the COVID-19 pandemic, limiting in-person professional activities resulting in the development of the novel remote training. We developed and evaluated a remote training approach for master trainers of the Caregiver Skills Training Program. Master trainers support community practitioners, who in turn deliver the Caregiver Skills Training program to caregivers of children with developmental delays or disabilities. The aim of this study was to evaluate the remote training of master trainers on Caregiver Skills Training Program. Twelve out of the 19 practitioners who enrolled in the training completed the study. The training consisted of a 5-day in-person session completed prior to the pandemic, followed by supporting participants’ ability to identify Caregiver Skills Training Program strategies through supported coding of seven video recordings over 7 weekly meetings and group discussions and ended with participants independently coding a set of 10 videos for Caregiver Skills Training Program strategies. We found that master trainers’ scoring reliability varied over 7 weeks of supported coding. All but one participant reached moderate or good independent scoring reliability despite a lack of ability to practice the Caregiver Skills Training Program strategies with children due to the pandemic. Taken together, our findings illustrate the feasibility and value of remote training approaches in implementing interventions. Lay Abstract The COVID-19 pandemic interrupted in-person professional activities. We developed and evaluated a remote training approach for master trainers of the Caregiver Skills Training Program. Master trainers support community practitioners, who in turn deliver the Caregiver Skills Training Program to caregivers of children with developmental delays or disabilities. The Caregiver Skills Training Program teaches caregivers how to use strategies to enhance learning and interactions during everyday play and home activities and routines with their child. The aim of this study was to evaluate the remote training of master trainers on Caregiver Skills Training Program. Twelve out of the 19 practitioners who enrolled in the training completed the study. The training consisted of a 5-day in-person session completed prior to the pandemic, followed by supporting participants’ ability to identify Caregiver Skills Training Program strategies through coding of video recordings over 7 weekly meetings and group discussions and ended with participants independently coding a set of 10 videos for Caregiver Skills Training Program strategies. We found all but one participant was able to reliably identify Caregiver Skills Training Program strategies from video recordings despite a lack of ability to practice the Caregiver Skills Training Program strategies with children due to the pandemic. Taken together, our findings illustrate the feasibility and value of remote training approaches in implementing interventions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.287
GPT teacher head0.450
Teacher spread0.163 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes2
Has abstractyes

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