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Record W2147178007 · doi:10.1080/13218719.2015.1032950

The Effects of Distributed Learning on Enhanced Cognitive Interview Training

2015· article· en· W2147178007 on OpenAlexaff
Constance Tamara Heidt, Katherine D. Arbuthnott, Heather L. Price

Bibliographic record

VenuePsychiatry Psychology and Law · 2015
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of ReginaCampion CollegeUniversity of Saskatchewan
Fundersnot available
KeywordsInterviewTraining (meteorology)Session (web analytics)CognitionPsychologyCognitive interviewCognitive trainingMedical educationApplied psychologyMotivational interviewingComputer scienceMedicinePsychiatryIntervention (counseling)World Wide Web

Abstract

fetched live from OpenAlex

Interview training for police officers is generally limited and, when it occurs, rarely translates into optimal interviews. Training ineffectiveness may be partly due to the structure of the training programme. In the present study, 60 participants received two hours of training on the Enhanced Cognitive Interview (ECI), in either a massed (one two-hour session) or spaced (two one-hour sessions) format. Following training, participants conducted an ECI. Advantages for spaced training were found in open-ended prompt use, perpetrator-specific details elicited from open prompts, and the utilization of two critical ECI components. These results suggest that a simple alteration in training protocols could improve forensic interviewing skills.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.349
Teacher spread0.311 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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