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Record W2895725629 · doi:10.28933/ijoar-2018-08-0601

Changing Roles of Care Team Members within New Models of Care Delivery in Residential Care Facilities: Implications for the Delivery of Quality of Care

2018· article· en· W2895725629 on OpenAlexaffabout

Bibliographic record

VenueInternational Journal of Aging Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of VictoriaIsland HealthInterior Health
Fundersnot available
KeywordsStaffingMentorshipNursingHealth careInterpersonal communicationTeamworkBusinessQuality (philosophy)Transformational leadershipPopulation healthPopulationPublic relationsMedicinePsychologyPolitical sciencePublic healthMedical educationEnvironmental health

Abstract

fetched live from OpenAlex

Providing quality of care (QoC) to older adults in residential care settings is an ongoing challenge given the increasingly complex needs of this population and the escalating economic constraints within which health authorities operate. While the implementation of the residential care delivery model in a Western Canadian health authority has contributed to some improvements in QoC, it has also highlighted key challenges that are both interpersonal and organizational in nature; specifically, gaps in leadership, teamwork, mentorship, and communication, as well as staffing mix, staffing consistency, resident complexity, and competing policy and program initiatives and directives. The implementation of a major change in care delivery impacts residents, families, and staff and may, in turn, impact their perceptions of change in QoC. When evaluating a model, therefore, it is important to examine both qualitative and quantitative outcomes: stories from those most affected in their everyday lives and trends in QoC indicator data.

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)0.028
metaresearch head score (Gemma)0.051
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 score0.038
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0100.009
Open science0.0040.008
Research integrity0.0020.004
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 head0.142
GPT teacher head0.503
Teacher spread0.361 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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