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Record W2803216717 · doi:10.1007/s10597-018-0286-0

Patient Perspectives on Strengths and Challenges of Therapist-Assisted Internet-Delivered Cognitive Behaviour Therapy: Using the Patient Voice to Improve Care

2018· article· en· W2803216717 on OpenAlexafffundabout
Heather D. Hadjistavropoulos, Y. Nichole Faller, A. Klatt, Marcie Nugent, Blake F. Dear, Nickolai Titov

Bibliographic record

VenueCommunity Mental Health Journal · 2018
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Regina
FundersCanadian Institutes of Health ResearchSaskatchewan Health Research FoundationHealth Research Foundation
KeywordsTimelineCognitionDownloadReading (process)PsychologyAnxietyThe InternetHealth psychologyMental healthPsychotherapistMedicineMedical educationNursingPsychiatryPublic health

Abstract

fetched live from OpenAlex

Therapist-assisted internet-delivered cognitive behaviour therapy (T-ICBT) involves patients reading online treatment materials, completing relevant exercises, and receiving therapist support. This study aimed to understand the preferences and recommendations of 225 patients enrolled in a T-ICBT course for depression and anxiety via an online therapy unit in collaboration with community mental health clinics dispersed across one Canadian province. An open-ended survey asked participants their opinions of the course and responses were analyzed using a content analysis approach. Patient comments addressed many strengths of the course (64%), with some opportunities for improvement (36%). Most-appreciated features included ability to download content for future use, reading other patients' experiences, and content of lessons. Patients made suggestions for improving the breadth of patient stories, timeline of the course, and matching availability of the therapist to patient need. Patient feedback regarding preferences provides valuable information for improving the patient-centered nature of T-ICBT.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.422
Teacher spread0.330 · 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 designQualitative
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

Citations43
Published2018
Admission routes3
Has abstractyes

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