Dataset Information

Using the Internet to Train Therapists: Randomized Comparison of Two Scalable Methods.

ABSTRACT:

Background

One of the major barriers to the dissemination and implementation of psychological treatments is the scarcity of suitably trained therapists. The currently accepted method of training is not scalable. Recently, a scalable form of training, Web-centered training, has been shown to have promise.

Objective

The goal of our research was to conduct a randomized comparison of the relative effects of independent and supported Web-centered training on therapist competence and investigate the persistence of the effects.

Methods

Eligible therapists were recruited from across the United States and Canada. They were randomly assigned to 1 of 2 forms of training in enhanced cognitive behavior therapy (CBT-E), a multicomponent evidence-based psychological treatment for any form of eating disorder. Independent training was undertaken autonomously, while supported training was accompanied by support from a nonspecialist worker. Therapist competence was assessed using a validated competence measure before training, after 20 weeks of training, and 6 months after the completion of training.

Results

A total of 160 therapists expressed interest in the study, and 156 (97.5%) were randomized to the 2 forms of training (81 to supported training and 75 to independent training). Mixed effects analysis showed an increase in competence scores in both groups. There was no difference between the 2 forms of training, with mean difference for the supported versus independent group being -0.06 (95% Cl -1.29 to 1.16, P=.92). A total of 58 participants (58/114, 50.9%) scored above the competence threshold; three-quarters (43/58, 74%) had not met this threshold before training. There was no difference between the 2 groups in the odds of scoring over the competence threshold (odds ratio [OR] 1.02, 95% CI 0.52 to 1.99; P=.96). At follow-up, there was no significant difference between the 2 training groups (mean difference 0.19, 95% Cl -1.27 to 1.66, P=.80). Overall, change in competence score from end of training to follow-up was not significant (mean difference -0.70, 95% CI -1.52 to 0.11, P=.09). There was also no difference at follow-up between the training groups in the odds of scoring over the competence threshold (OR 0.95, 95% Cl 0.34 to 2.62; P=.92).

Conclusions

Web-centered training was equally effective whether undertaken independently or accompanied by support, and its effects were sustained. The independent form of Web-centered training is particularly attractive as it provides a means of training large numbers of geographically dispersed therapists at low cost, thereby overcoming several obstacles to the widespread dissemination of psychological treatments.

SUBMITTER: Cooper Z

PROVIDER: S-EPMC5666223 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Publications

Using the Internet to Train Therapists: Randomized Comparison of Two Scalable Methods.

Cooper Zafra Z Bailey-Straebler Suzanne S Morgan Katy E KE O'Connor Marianne E ME Caddy Caroline C Hamadi Layla L Fairburn Christopher G CG

Journal of medical Internet research 20171018 10

<h4>Background</h4>One of the major barriers to the dissemination and implementation of psychological treatments is the scarcity of suitably trained therapists. The currently accepted method of training is not scalable. Recently, a scalable form of training, Web-centered training, has been shown to have promise.<h4>Objective</h4>The goal of our research was to conduct a randomized comparison of the relative effects of independent and supported Web-centered training on therapist competence and in ...[more]

PMID: 29046265

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Project description:BackgroundIn some settings, research methods to determine influenza vaccine effectiveness (VE) may not be appropriate because of cost, time constraints, or other factors. Administrative database analysis of viral testing results and vaccination history may be a viable alternative. This study compared VE estimates from outpatient research and administrative databases.MethodsUsing the test-negative, case-control design, data for 2017-2018 and 2018-2019 influenza seasons were collected using: 1) consent, specimen collection, RT-PCR testing and vaccine verification using multiple methods; and 2) an administrative database of outpatients with a clinical respiratory viral panel combined with electronic immunization records. Odds ratios for likelihood of influenza infection by vaccination status were calculated using multivariable logistic regression. VE = (1 - aOR) × 100.ResultsResearch participants were significantly younger (P < 0.001), more often white (69% vs. 59%; P < 0.001) than non-white and less frequently enrolled through the emergency department (35% vs. 72%; P < 0.001) than administrative database participants. VE was significant against all influenza and influenza A in each season and both seasons combined (37-49%). Point estimate differences between methods were evident, with higher VE in the research database, but insignificant due to low sample sizes. When enrollment sites were separately analyzed, there were significant differences in VE estimates for all influenza (66% research vs. 46% administrative P < 0.001) and influenza A (67% research vs. 49% administrative; P < 0.001) in the emergency department.ConclusionsThe selection of the appropriate method for determining influenza vaccine effectiveness depends on many factors, including sample size, subgroups of interest, etc., suggesting that research estimates may be more generalizable. Other advantages of research databases for VE estimates include lack of clinician-related selection bias for testing and less misclassification of vaccination status. The advantages of the administrative databases are potentially shorter time to VE results and lower cost.

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Dataset Information

Using the Internet to Train Therapists: Randomized Comparison of Two Scalable Methods.

Background

Objective

Methods

Results

Conclusions

Publications

Using the Internet to Train Therapists: Randomized Comparison of Two Scalable Methods.

Similar Datasets

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