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Record W2577173282 · doi:10.2196/mental.5878

Tips and Traps: Lessons From Codesigning a Clinician E-Monitoring Tool for Computerized Cognitive Behavioral Therapy

2017· article· en· W2577173282 on OpenAlexvenueno aff
Frederick Sundram, Susan J. Hawken, Karolina Stasiak, Mathijs Lucassen, Theresa Fleming, Matthew Shepherd, Andrea Greenwood, Raechel Osborne, Sally Merry

Bibliographic record

VenueJMIR Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersHealth Research Council of New Zealand
KeywordsCognitive behavioral therapyPsychologyCognitionBehavioral therapyMedicineMedical physicsClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: Computerized cognitive behavioral therapy (cCBT) is an acceptable and promising treatment modality for adolescents with mild-to-moderate depression. Many cCBT programs are standalone packages with no way for clinicians to monitor progress or outcomes. We sought to develop an electronic monitoring (e-monitoring) tool in consultation with clinicians and adolescents to allow clinicians to monitor mood, risk, and treatment adherence of adolescents completing a cCBT program called SPARX (Smart, Positive, Active, Realistic, X-factor thoughts). OBJECTIVE: The objectives of our study were as follows: (1) assess clinicians' and adolescents' views on using an e-monitoring tool and to use this information to help shape the development of the tool and (2) assess clinician experiences with a fully developed version of the tool that was implemented in their clinical service. METHODS: A descriptive qualitative study using semistructured focus groups was conducted in New Zealand. In total, 7 focus groups included clinicians (n=50) who worked in primary care, and 3 separate groups included adolescents (n=29). Clinicians were general practitioners (GPs), school guidance counselors, clinical psychologists, youth workers, and nurses. Adolescents were recruited from health services and a high school. Focus groups were run to enable feedback at 3 phases that corresponded to the consultation, development, and postimplementation stages. Thematic analysis was applied to transcribed responses. RESULTS: Focus groups during the consultation and development phases revealed the need for a simple e-monitoring registration process with guides for end users. Common concerns were raised in relation to clinical burden, monitoring risk (and effects on the therapeutic relationship), alongside confidentiality or privacy and technical considerations. Adolescents did not want to use their social media login credentials for e-monitoring, as they valued their privacy. However, adolescents did want information on seeking help and personalized monitoring and communication arrangements. Postimplementation, clinicians who had used the tool in practice revealed no adverse impact on the therapeutic relationship, and adolescents were not concerned about being e-monitored. Clinicians did need additional time to monitor adolescents, and the e-monitoring tool was used in a different way than was originally anticipated. Also, it was suggested that the registration process could be further streamlined and integrated with existing clinical data management systems, and the use of clinician alerts could be expanded beyond the scope of simply flagging adolescents of concern. CONCLUSIONS: An e-monitoring tool was developed in consultation with clinicians and adolescents. However, the study revealed the complexity of implementing the tool in clinical practice. Of salience were privacy, parallel monitoring systems, integration with existing electronic medical record systems, customization of the e-monitor, and preagreed monitoring arrangements between clinicians and adolescents.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.227
GPT teacher head0.553
Teacher spread0.326 · 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 designOther design
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

Citations40
Published2017
Admission routes1
Has abstractyes

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