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Bathing residents with dementia in long‐term care: critical incidents described by personal support workers

2011· article· en· W1947355080 on OpenAlexaffabout
A.D.F. Dhondt, Sharon Kaasalainen, Dawn Prentice, Lori Schindel Martin

Bibliographic record

VenueInternational Journal of Older People Nursing · 2011
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsBathingDementiaLong-term carePersonal carePsychologyTerm (time)MedicineNursingNursing homesGerontologyFamily medicineDisease

Abstract

fetched live from OpenAlex

AIM AND OBJECTIVE: The aim of this study was to describe and gain insight into the critical incidents depicted by personal support workers (PSWs) in long-term care (LTC) related to bathing residents who have dementia. BACKGROUND: Residents with dementia in LTC often display responsive/protective behaviours during bathing. Consequently, bathing is a source of stress for PSWs who provide most of the personal care for LTC residents in Ontario, Canada. DESIGN: A qualitative descriptive study employing the critical incident technique (CIT) was used. METHOD: Eight PSWs were interviewed and 24 incidents were collected and analyzed using thematic content analysis. RESULTS: Findings revealed that PSWs experience the following during bathing: managing responsive/protective behaviours, working with limited resources, and dealing with communication difficulties. Participants used various strategies to respond to these challenges; however, they reported limited strategies to manage the most challenging behaviours. CONCLUSIONS: Many of the bathing strategies described by the PSWs in this study are found in the literature about best bathing practices in dementia care. However, it is evident that further work is needed to support PSWs to manage the most difficult physical responsive/protective behaviours that occur during bathing. IMPLICATIONS FOR PRACTICE: This study has clear implications for knowledge translation.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.419
Teacher spread0.372 · 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

Citations21
Published2011
Admission routes2
Has abstractyes

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