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Record W2040738594 · doi:10.1002/jclp.20232

Developing an evidence base in clinical psychology

2005· article· en· W2040738594 on OpenAlexaff
Karina W. Davidson, Bonnie Spring

Bibliographic record

VenueJournal of Clinical Psychology · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsColumbia College
FundersNational Heart, Lung, and Blood Institute
KeywordsReimbursementPsychologyProcess (computing)Evidence-based practiceClinical PracticeField (mathematics)Applied psychologyHealth careAlternative medicineMedicineComputer scienceNursingLaw

Abstract

fetched live from OpenAlex

We suggest a process for clinical psychologists to collect an evidence base and join the evidence-based movement already underway in many areas of medicine. To illustrate this process, we review the history of cholesterol discovery, evaluation, and management as an evidence-based process, extracting lessons applicable to the field of psychology. By examining these lessons and building consensus, clinical psychologists can advance the movement along an evidence-based practice continuum, improve client care, build a more informative evidence base, and promote equitable reimbursement for psychological practice.

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)0.530
metaresearch head score (Gemma)0.741
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.470
Threshold uncertainty score0.580

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5300.741
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0120.013
Bibliometrics0.0600.023
Science and technology studies0.0060.012
Scholarly communication0.0310.035
Open science0.0160.023
Research integrity0.0280.033
Insufficient payload (model declined to judge)0.0070.002

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 head0.977
GPT teacher head0.790
Teacher spread0.187 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations11
Published2005
Admission routes1
Has abstractyes

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