Development and validation of a measurement tool to assess perceptions of palliative care
Bibliographic record
Abstract
OBJECTIVES: Understanding patient's perceptions about palliative care is necessary to make an effective referral. The aim was to develop and validate a measure of patient perceptions. METHODS: Items were generated through patient, family caregiver and health professional interviews. The Perceptions of Palliative Care Instrument (PPCI) was administered to 85 patients with advanced cancer (AC). A subset (n = 39) completed it 7 days later. Participants also completed the Palliative Care Outcome Scale (POS), the Edmonton Symptom Assessment Scale (ESAS) and the Distress Thermometer (DT) for validation purposes. RESULTS: Factor analysis revealed four domains (positive and negative emotional and cognitive reactions to palliative care, emotional and practical palliative care needs and perceptions of health) and eight subscales with factor loadings for all items above 0.51, explaining 61-81% of the total variance. Reliability analyses revealed high internal consistency (Cronbach's alpha coefficients >0.76). Intraclass correlation coefficients showed moderate to strong correlations between time points indicating stability over time. All POS items correlated with two or more dimensions of the PPCI (all r > 0.30); ESAS total distress correlated positively with the palliative care needs domain and the DT with needs and perceived burden (all r > 0.30). CONCLUSIONS: The PPCI is a four-factor, 37-item measure that assesses perceptions of palliative care held by patients with AC. The measure has good internal consistency, test-retest reliability and convergent validity.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".