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Record W4283786526 · doi:10.1093/eurjcn/zvac060.074

Development and psychometric properties of a short version of the Patient Continuity of Care Questionnaire

2022· article· en· W4283786526 on OpenAlexaff
Emma Säfström, Kristofer Årestedt, Heather D. Hadjistavropoulos, Maria Liljeroos, Lena Nordgren, Tiny Jaarsma, Anna Strömberg

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of Regina
FundersMedical Research CouncilUppsala UniversitetForskningsrådet i Sydöstra Sverige
KeywordsMedicineRasch modelMyocardial infarctionHealth careFamily medicinePsychiatryPsychology

Abstract

fetched live from OpenAlex

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Medical Research Council of Southeast Sweden. Center for Clinical Research Sörmland/Uppsala University Introduction Hundreds of thousands of persons are discharged from hospitalisation due to cardiac diseases every year in Europe. Follow-up after hospitalisation due to cardiac diseases usually occurs with another healthcare provider, e.g., in primary care, which is a challenge for the coordination and continuity of care. Continuity of care after hospitalisation is an essential indicator of the quality of care, and international reports highlight the importance of improving continuity of care. Patients’ perception of continuity of care can be evaluated using the Patient Continuity of Care Questionnaire (PCCQ-27 items). However, the length of the questionnaire represents a barrier to completion, and, therefore, we aimed to develop and psychometrically evaluate a short version of the PCCQ. Method This was a psychometric validation study. Content validity was first evaluated among user groups, including patients (n=7), health care professionals (n=15), and researchers (n=7). Then, based on content validity and conceptual discussions in the research group, 12 items were selected for the short version. Data were collected from patients six weeks after hospitalisation due to angina, atrial fibrillation, acute myocardial infarction, or heart failure using a consecutive sampling procedure. Measurement properties were evaluated with the Rasch Measurement Model. Results A total of 1000 patients were included (66% males, mean age 72 SD=10). The 12 items presented satisfactory overall model fit and a reliability of 0.79. Three items did not fit the model as they presented fit residuals >±2.5. The items presenting misfit were: item 12 (information on treatment after discharge), item 28 (personnel had knowledge on medical situation) and item 29 (confidence in personnel) (Table 1). Four items presented response dependence; a patients answer on item 2 (information on prognosis) seemed to depend on how the patient had answered item 1 (information on diagnosis). Also, items 28 and 29 seemed to be response dependent. Each pair of response-dependent items was combined into a larger subtest item to evaluate any impact on measurement properties. However, the changes in person location, person fit, and PSI were negligible, and no effect on the measurement properties or reliability was observed. Further, no evidence of multidimensionality was found, and a total score can be calculated. The Rasch measurement model found the thresholds between the first two response options ‘strongly disagree’ and ‘somewhat disagree’ disordered in all items (Table 1). However, we found that amending the response options did not alter the prior results regarding response dependence, dimensionality, or reliability but improved individual fit residual for items 12, 27 and 28. Conclusion The PCCQ-12 is a short, unidimensional and psychometrically sound questionnaire ready to be used to measure the perception of continuity of care after hospitalisation.

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)0.016
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.049
GPT teacher head0.318
Teacher spread0.269 · 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 designBench or experimental
Domainnot available
GenreMethods

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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