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Record W2329481491 · doi:10.1037/a0032408

Computers and psychotherapy: Are we out of a job?

2013· letter· en· W2329481491 on OpenAlexaff
Marna S. Barrett, Marina Gershkovich

Bibliographic record

VenuePsychotherapy · 2013
Typeletter
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsSTART Clinic
FundersNational Institute of Mental Health
KeywordsAlliancePsychologyPsychotherapistRandomized controlled trialThe InternetClinical psychologyMedicineWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Over the past 15 years, technology has increasingly been incorporated into the provision of psychotherapy with studies emerging demonstrating the effectiveness of such models. However, randomized controlled trials remain scant and little is known about the impact of computer technology on the therapeutic alliance. The studies reported in this section are among the first randomized clinical trials of computer-assisted or internet-based therapies. The following commentary provides a brief overview of each paper and highlights the key issues involved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.058
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0050.011
Open science0.0030.002
Research integrity0.0580.046
Insufficient payload (model declined to judge)0.0080.004

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 head0.063
GPT teacher head0.387
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations9
Published2013
Admission routes1
Has abstractyes

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