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Record W2119848026 · doi:10.1177/1049731512459966

Development and Initial Evaluation of the Cyber-Counseling Objective Structured Clinical Examination (COSCE)

2012· article· en· W2119848026 on OpenAlexaff
Lin Fang, Marion Bogo, Faye Mishna, Lawrence Murphy, Margaret F. Gibson, Valeska Griffiths, Glenn Regehr

Bibliographic record

VenueResearch on Social Work Practice · 2012
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsInter-rater reliabilityPsychologyCompetence (human resources)Exploratory factor analysisRating scaleInternal consistencyApplied psychologyConstruct validityPsychometricsClinical psychologyMedical educationMedicineSocial psychology

Abstract

fetched live from OpenAlex

Objectives: This study developed and validated the Cyber-Counseling Objective Structured Clinical Examination (COSCE), a method and tool used to assess the competence level of trainees and professionals who practice cyber-counseling. Method: The COSCE’s development involved the creation of a cyber-counseling performance rating scale and two simulated client scenarios, and the recruitment and training of three raters. The COSCE was tested on six masters of social work students and six seasoned cyber-counseling practitioners. Results: We examined the COSCE’s internal consistency, interrater reliability, and interclient reliability. In addition, we assessed the construct validity through exploratory factor analysis and known-groups validation method. Conclusions: With further improvement, the COSCE can be a reliable and valid tool in assessing the competence of cyber-counseling practitioners.

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)0.031
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.379
GPT teacher head0.573
Teacher spread0.194 · 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 designBench or experimental
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

Citations13
Published2012
Admission routes1
Has abstractyes

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