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Record W1836231218 · doi:10.1002/acp.2822

Transfer of Skills in the Context of Non‐Suggestive Investigative Interviews: Impact of Structured Interview Protocol and Feedback

2012· article· en· W1836231218 on OpenAlexaff
Mireille Cyr, Jacinthe Dion, Pierre McDuff, Karine Trotier‐Sylvain

Bibliographic record

VenueApplied Cognitive Psychology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversité du Québec à ChicoutimiUniversité de Montréal
FundersNational Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsPsychologyContext (archaeology)Protocol (science)Cognitive interviewSemi-structured interviewTransfer of trainingInterviewApplied psychologyQualitative researchCognitionCognitive psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Summary Two groups of police investigators were trained in the use of the National Institute of Child Health and Human Development (NICHD) interview protocol. After the training, one group (n = 8) received written feedback on each interview they conducted, whereas the other group (n = 11) did not. The objective of the study was, first, to evaluate the effect of NICHD protocol implementation on the types of questions and details provided by children and, second, to evaluate the impact of post‐training feedback. Interviewees were alleged sexual abuse victims between the ages of 3 and 14 years. Application of the NICHD protocol allowed interviewers to use more open‐ended questions and to obtain more details. The results show that providing feedback significantly increased the quality of the interviews. It is recommended that the NICHD protocol be used to interview child victims and witnesses and that feedback sessions be held with interviewers. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.358
metaresearch head score (Gemma)0.602
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3580.602
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0040.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.389
Teacher spread0.320 · 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.

Study designNon-randomized trial
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

Citations54
Published2012
Admission routes1
Has abstractyes

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