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Record W4220840370 · doi:10.1080/24732850.2022.2044714

Police perspectives on interviewing older adult victims and witnesses: Preliminary findings and call for future research

2022· article· en· W4220840370 on OpenAlexafffundabout
Mark Snow, Joshua Wyman, Lindsay C. Malloy, Sonja P. Brubacher, Kelly L. Warren

Bibliographic record

VenueJournal of Forensic Psychology Research and Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsMemorial University of NewfoundlandOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInterviewShameUnavailabilityPsychologyQualitative researchSample (material)PopulationCriminologyMedical educationMedicinePolitical scienceSocial psychologySociologyLawEngineering

Abstract

fetched live from OpenAlex

As the proportion of older adults (OAs) in the population continues to increase, so too will the frequency of police interactions with OAs and the need to gather accurate and detailed accounts from them. Yet, research on police information-gathering with OAs remains relatively scarce. This qualitative study begins to address this gap. We conducted semi-structured interviews with an experienced Canadian police sample (N = 10) regarding their involvement with, and perspectives on, interviewing OA victims and witnesses. Participants reported heterogeneous interactions with OAs, identified barriers that OAs face (e.g., shame), and affirmed the unavailability of training and official guidance on information-gathering with OAs. Our findings highlight existing practical challenges and future research avenues.

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)0.045
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0180.011
Scholarly communication0.0100.013
Open science0.0030.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.132
GPT teacher head0.513
Teacher spread0.381 · 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 designQualitative
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

Citations5
Published2022
Admission routes3
Has abstractyes

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