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Implementation of the abilities‐focused approach to morning care of people with dementia by nursing staff

2009· article· en· W2053636306 on OpenAlexafffund
Souraya Sidani, Chantale LeClerc, David L. Streiner

Bibliographic record

VenueInternational Journal of Older People Nursing · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsToronto Metropolitan UniversityBaycrest Hospital
FundersAlzheimer SocietyCanadian Nurses Foundation
KeywordsPsychological interventionAttendanceSession (web analytics)NursingMorningDementiaMedicineNursing Interventions ClassificationConversationPsychology

Abstract

fetched live from OpenAlex

Background. The efficacy of the abilities-focused approach to morning care has been demonstrated in two studies. However, the extent to which nurses are aware of and actually implement abilities-focused interventions in day-to-day practice is not known. Aim. The study aimed to determine the type and number of abilities-focused interventions delivered by nursing staff to residents with dementia during morning care. Methods. A one-group repeated measure design was used. Seventy-nine nursing staff attended an educational session to instruct them in the application of abilities-focused interventions. Data on the type and number of interventions used by nurses were obtained before, after and 3-months following attendance at the session. Data were collected through participants' self-report and observation. Results. Most nursing staff used abilities-focused interventions when providing morning care. Introduction to resident, orientation to resident and conversation with resident were three types of interventions most often applied over time. The number of interventions implemented increased after attendance at the education session and returned to baseline level at 3-month follow-up. Conclusions. Future research is recommended to examine the long-term effects of alternative designs of educational sessions.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.460

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 head0.016
GPT teacher head0.372
Teacher spread0.356 · 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 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

Citations19
Published2009
Admission routes2
Has abstractyes

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