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Record W2014438985

Management continuity from the patient perspective: comparison of primary healthcare evaluation instruments.

2011· article· en· W2014438985 on OpenAlexaff
Jeannie Haggerty, Fred Burge, Raynald Pineault, Marie‐Dominique Beaulieu, Fatima Bouharaoui, Christine Beaulieu, Darcy A. Santor, Jean‐Frédéric Lévesque

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrimary careHealth carePatient satisfactionVeterans AffairsExploratory factor analysisPsychologyMedicineAmbulatory careFamily medicineNursingPsychometricsClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

UNLABELLED: Management continuity, operationally defined as "the extent to which services delivered by different providers are timely and complementary such that care is experienced as connected and coherent," is a core attribute of primary healthcare. Continuity, as experienced by the patient, is the result of good care coordination or integration. OBJECTIVE: To provide insight into how well management continuity is measured in validated coordination or integration subscales of primary healthcare instruments. METHOD: Relevant subscales from the Primary Care Assessment Survey (PCAS), the Primary Care Assessment Tool - Short Form (PCAT-S), the Components of Primary Care Instrument (CPCI) and the Veterans Affairs National Outpatient Customer Satisfaction Survey (VANOCSS) were administered to 432 adult respondents who had at least one healthcare contact with a provider other than their family physician in the previous 12 months. Subscales were examined descriptively, by correlation and factor analysis and item response theory analysis. Because the VANOCSS elicits coordination problems and is scored dichotomously, we used logistic regression to examine how evaluative subscales relate to reported problems. RESULTS: Most responses to the PCAS, PCAT-S and CPCI subscales were positive, yet 83% of respondents reported having one or more problems on the VANOCSS Overall Coordination subscale and 41% on the VANOCSS Specialist Access subscale. Exploratory factor analysis suggests two distinct factors. The first (eigenvalue=6.98) is coordination actions by the primary care physician in transitioning patient care to other providers (PCAS Integration subscale and most of the PCAT-S Coordination subscale). The second (eigenvalue=1.20) is efforts by the primary care physician to create coherence between different visits both within and outside the regular doctor's office (CPCI Coordination subscale). The PCAS Integration subscale was most strongly associated with lower likelihood of problems reported on the VANOCSS subscales. CONCLUSION: Ratings of management continuity correspond only modestly to reporting of coordination problems, possibly because they rate only the primary care physician, whereas patients experience problems across the entire system. The subscales were developed as measures of integration and provider coordination and do not capture the patient's experience of connectedness and coherence.

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)0.021
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.164
GPT teacher head0.407
Teacher spread0.243 · 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 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

Citations24
Published2011
Admission routes1
Has abstractyes

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