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Record W4412002943 · doi:10.3928/00989134-20250627-01

Challenging Behaviors in Nursing Homes: Impact of Staff Training on Laypeople's Perceptions of Staff Competence

2025· article· en· W4412002943 on OpenAlexaff
Laura Deprez, Vincent Didone, Effrosyni Pyrovolaki, Stéphane Adam

Bibliographic record

VenueJournal of Gerontological Nursing · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsCompetence (human resources)AttractivenessNursingVignetteFeelingPerceptionPsychologyNursing staffMedicineSocial psychology

Abstract

fetched live from OpenAlex

PURPOSE: Challenging behaviors of residents with dementia represent a major concern in nursing homes (NHs). Although various studies have investigated the effects of staff training in challenging behavior management, few have explored their impact on laypeople's perceptions. However, NH professionals routinely interact with laypeople, such as current and prospective residents and their families, volunteers, and community partners. The current study examined the effect of person-centered staff training on naïve individuals' perceptions of staff competence. METHOD: Twenty-two NH professionals completed person-centered care training in challenging behavior management and responded to a clinical vignette before and after training. Their responses were evaluated by 59 naïve assessors for relevance, confidence, and competence. In addition, two trained assessors were recruited to explore whether their informed evaluations corroborated naïve assessors' impressions. RESULTS: Relevance, confidence, and competence improved significantly according to naïve and trained assessors. NH professionals also reported feeling more competent. CONCLUSION: Multiple measures indicate improved staff performance following training. Future studies should examine how laypeople's perceptions of staff competence influence their experiences in NHs and impact NH attractiveness.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.062
GPT teacher head0.460
Teacher spread0.397 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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