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Record W2038526019 · doi:10.3928/00989134-20140110-02

Using a Book Chat to Improve Attitudes and Perceptions of Long-Term Care Staff About Dementia

2014· article· en· W2038526019 on OpenAlexfundaboutno aff
Natasha Larocque, Chloe Schotsman, Sharon Kaasalainen, D Crawshaw, Carrie McAiney, Emma Brazil

Bibliographic record

VenueJournal of Gerontological Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsDementiaThematic analysisPerceptionPsychologyQualitative researchLong-term careContent analysisNursingIntervention (counseling)Medical educationMedicineDiseaseSociology

Abstract

fetched live from OpenAlex

This study sought to evaluate a book chat intervention based on Lisa Genova's novel, Still Alice, to influence long-term care (LTC) staff perceptions and attitudes when caring for individuals with dementia. A qualitative descriptive design was used. Eleven participants partook in a 2.5-hour book chat at a southern Ontario LTC facility. Following the book chat, participants answered two open-ended questions to assess how the book chat influenced their views on dementia. Thematic content analysis was used to analyze the qualitative questionnaire. Content analysis of the participants' responses revealed that the book chat positively influenced their attitudes and perceptions toward dementia, particularly by providing more insight into the individual's personal struggle with the disease. Furthermore, participants found that the book chat influenced their care practices. By creating innovative learning opportunities, attitudes and perceptions about dementia care can be transcended and greatly benefit staff, family, and residents.

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 score0.309
Threshold uncertainty score0.346

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.383
Teacher spread0.353 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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