Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis
Theoretical backgroundResearch of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of diff...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1296449/full |
| _version_ | 1827318712225497088 |
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| author | Robert Staeck Robert Staeck Miriam Stüble Miriam Stüble Marie Drüge |
| author_facet | Robert Staeck Robert Staeck Miriam Stüble Miriam Stüble Marie Drüge |
| author_sort | Robert Staeck |
| collection | DOAJ |
| description | Theoretical backgroundResearch of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of different EMH services and applications. Therefore, the aims of this study were to investigate the factors that predict acceptance of EMH among a sample of PiT using a latent class analysis. The study will 1) determine how many acceptance prediction classes can be distinguished and 2) describe classes and differences between classes based on their characteristics.MethodsA secondary analysis of a cross-sectional online survey was conducted. N = 216 PiT (88.4% female) participated. In the study, participants were asked to rate their acceptance of EMH, as operationalized by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, along with its predictors, perceived barriers, perceived advantages and additional facilitators. Indicator variables for the LCA were eight items measuring the UTAUT-predictors.ResultsBest model fit emerged for a two-class solution; the first class showed high levels on all UTAUT-predictors, the second class revealed moderate levels on the UTAUT-predictors.ConclusionThis study was able to show that two classes of individuals can be identified based on the UTAUT-predictors. Differences between the classes regarding Performance Expectancy and Effort Expectancy were found. Interestingly, the two classes differed in theoretical orientation but not in age or gender. Latent class analysis could help to identify subgroups and possible starting points to foster acceptance of EMH. |
| first_indexed | 2024-04-25T00:06:35Z |
| format | Article |
| id | doaj.art-0fda1e4cc35543de89c6b9f1681b3b4c |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-25T00:06:35Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-0fda1e4cc35543de89c6b9f1681b3b4c2024-03-14T05:08:03ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402024-03-011510.3389/fpsyt.2024.12964491296449Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysisRobert Staeck0Robert Staeck1Miriam Stüble2Miriam Stüble3Marie Drüge4University of Bern, Faculty of Medicine, Institute of Social and Preventive Medicine, Bern, SwitzerlandGraduate School for Health Sciences, University of Bern, Bern, SwitzerlandGraduate School for Health Sciences, University of Bern, Bern, SwitzerlandUniversity Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, SwitzerlandUniversity of Zurich, Department of Psychology, Clinical Psychology with Focus on Psychotherapy Research, Zurich, SwitzerlandTheoretical backgroundResearch of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of different EMH services and applications. Therefore, the aims of this study were to investigate the factors that predict acceptance of EMH among a sample of PiT using a latent class analysis. The study will 1) determine how many acceptance prediction classes can be distinguished and 2) describe classes and differences between classes based on their characteristics.MethodsA secondary analysis of a cross-sectional online survey was conducted. N = 216 PiT (88.4% female) participated. In the study, participants were asked to rate their acceptance of EMH, as operationalized by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, along with its predictors, perceived barriers, perceived advantages and additional facilitators. Indicator variables for the LCA were eight items measuring the UTAUT-predictors.ResultsBest model fit emerged for a two-class solution; the first class showed high levels on all UTAUT-predictors, the second class revealed moderate levels on the UTAUT-predictors.ConclusionThis study was able to show that two classes of individuals can be identified based on the UTAUT-predictors. Differences between the classes regarding Performance Expectancy and Effort Expectancy were found. Interestingly, the two classes differed in theoretical orientation but not in age or gender. Latent class analysis could help to identify subgroups and possible starting points to foster acceptance of EMH.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1296449/fulle-mental healthUTAUTpsychotherapists-in-trainingacceptancelatent class analysis |
| spellingShingle | Robert Staeck Robert Staeck Miriam Stüble Miriam Stüble Marie Drüge Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis Frontiers in Psychiatry e-mental health UTAUT psychotherapists-in-training acceptance latent class analysis |
| title | Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis |
| title_full | Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis |
| title_fullStr | Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis |
| title_full_unstemmed | Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis |
| title_short | Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis |
| title_sort | exploring subgroups of acceptance prediction for e mental health among psychotherapists in training a latent class analysis |
| topic | e-mental health UTAUT psychotherapists-in-training acceptance latent class analysis |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1296449/full |
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