Adaptation and psychometric properties of the Norwegian version of the heart continuity of care questionnaire (HCCQ)
Bibliographic record
Abstract
BACKGROUND: Continuity of cardiac care after hospital discharge is a priority, especially as healthcare systems become increasingly complex and fragmented. There are few available instruments to measure continuity of cardiac care, especially from the patient perspective. The aim of this study was (1) to translate and adapt the Heart Continuity of Care Questionnaire (HCCQ) to conditions in Norway, and (2) to determine its psychometric properties in self-report format administered to patients after percutaneous coronary intervention (PCI). METHODS: The HCCQ was first translated into Norwegian from the original English version, following a widely used cross-cultural adaptation process. Data were collected before hospital discharge and in a follow-up after 2 months. To assess psychometric properties, a confirmatory factor analysis (CFA) was performed and three aspects of construct validity were evaluated: structural validity, hypotheses testing and cross-cultural validation. Internal consistency of the HCCQ subscales was calculated using Cronbach's alpha, while intra-class correlation (ICC) was used to assess test-retest reliability. Additionally, socio-demographic and patient-reported data were collected to correlate with HCCQ scores. RESULTS: Of those included at baseline, 436 (76%) completed the questionnaires after 2 months. CFA suggested that the fit of the HCCQ data to a 3-factor model was modest (RMSEA = 0.11, CFI = 0.90, TLI = 0.90). However, convergent validity was satisfactory, based on existing research. Internal consistency was good, as indicated by its Cronbach's alphas: total continuity of care (0.95); informational (0.93), relational (0.87), and management (0.89) continuity. The ICC for the total HCCQ score was 0.80 (95% CI [0.71, 0.87] p < 0.001). As indicated by negative care experiences (rated as 1 or 2 on the five-point scale), patients seemed to have limited knowledge about medical treatment, lifestyle modification and follow-up after PCI. Participation in cardiac rehabilitation and longer consultations with the general practitioner after hospital discharge were positively correlated with better continuity of care. CONCLUSIONS: Implementation of the HCCQ will likely support healthcare providers and researchers in identifying problem areas of continuity of cardiac care and in evaluating interventions aimed at improving continuity of care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".