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Record W4246878065 · doi:10.32920/ryerson.14656476

Is Long-Term Care Person Centred? A Case Study

2021· preprint· en· W4246878065 on OpenAlexaffabout
Katarina Young

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsToronto Metropolitan UniversityToronto Public HealthWestern University
Fundersnot available
KeywordsConceptualizationLong-term careGovernment (linguistics)LegislationAccreditationQualitative researchFocus groupPublic relationsNursingBest practiceBusinessMedicinePolitical scienceMedical educationSociologyMarketing

Abstract

fetched live from OpenAlex

In Ontario long-term care (LTC) settings, person-centred care (PCC) is promoted by government legislation, accreditation organizations and professional practice guidelines aiming to integrate this approach. However, there is currently no standardized approach to providing PCC in LTC. The purpose of this study was to examine public policies on PCC in Ontario and explore how they are interpreted and translated into practice in LTC. A qualitative case study approach was used to examine the perspectives of key stakeholders at one LTC facility in Ontario. Focus groups were conducted with residents, family members, direct care providers and managers. Through content analysis, findings were organized into four categories showcasing both overlapping and differential understandings of PCC in practice: 1) conceptualization, 2) barriers, 3) facilitators, and 4) evaluation. Identified tensions between policy and the delivery of PCC highlight systemic issues that must be addressed to enable equitable person-centred LTC rooted in resident-identified priorities.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.465
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.007
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.217
GPT teacher head0.442
Teacher spread0.226 · 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
Published2021
Admission routes2
Has abstractyes

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