MétaCan
Menu
Back to cohort
Record W2090559914 · doi:10.1177/1471301206059754

Components of coordinated care

2006· article· en· W2090559914 on OpenAlexaff
Rhonda Cockerill, Susan Jaglal, Louise Lemieux Charles, Larry W. Chambers, Kevin Brazil, Carole Cohen

Bibliographic record

VenueDementia · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsSunnybrook Health Science CentreSt. Joseph’s Healthcare HamiltonUniversity of OttawaMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsDyadDementiaPsychologyDiseaseConstruct (python library)Health careNursingFamily caregiversMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

This article reports on the development of an instrument to measure dementia patients' and their families' experiences with care provision. Using the responses of 267 care recipient/caregiver dyads, exploratory factor analysis was used to extract an underlying structure of the dyads' assessments of their experiences with dementia networks of care. The results suggested that from the perspective of the care recipient and caregiver, it is the individuals who they interact with in their care journey that define and shape the evaluation of their experiences. In the early stages of dementia, the family physician plays a central role in helping dyads understand the disease and the networks of care that are available to them; in later stages of the disease, it is the activities of the health care worker who is central to the dyad's lived experiences of the care they are receiving. The third important construct linked to the period when a care recipient and caregiver dyad was increasingly aware that dementia services may be needed and the process of assessment and placement was underway. Having information about what resources are available and how they can be accessed, and being able to complete assessments and placements in a timely fashion, was central to their assessment of care networks.

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 score0.018
Threshold uncertainty score0.990

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)0.0010.000

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 head0.060
GPT teacher head0.401
Teacher spread0.341 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueDementiaSame topicPatient Satisfaction in HealthcareFrench-language works237,207