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Record W3025660216 · doi:10.1002/jts.22544

Cognitive Processing Therapy for Posttraumatic Stress Disorder via Telehealth: Practical Considerations During the COVID‐19 Pandemic

2020· review· en· W3025660216 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Traumatic Stress · 2020
Typereview
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsToronto Metropolitan University
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthOffice of Research and DevelopmentPatient-Centered Outcomes Research InstituteU.S. Department of Veterans Affairs
KeywordsTelehealthPandemicTelepsychiatryMental healthCognitive processing therapyCoronavirus disease 2019 (COVID-19)CognitionTelemedicinePsychologyPosttraumatic stressPsychiatryCognitive therapyPsychotherapistHealth careMedicineClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

The global outbreak of COVID-19 has required mental health providers to rapidly rethink and adapt how they provide care. Cognitive processing therapy (CPT) is a trauma-focused, evidence-based treatment for posttraumatic stress disorder that is effective when delivered in-person or via telehealth. Given current limitations on the provision of in-person mental health treatment during the COVID-19 pandemic, this article presents guidelines and treatment considerations when implementing CPT via telehealth. Based on lessons learned from prior studies and clinical delivery of CPT via telehealth, recommendations are made with regard to overall strategies for adapting CPT to a telehealth format, including how to conduct routine assessments and ensure treatment fidelity.

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.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.354
GPT teacher head0.527
Teacher spread0.172 · 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