An instrument to measure nurses' knowledge in palliative care: Validation of the Spanish version of Palliative Care Quiz for Nurses
Bibliographic record
Abstract
BACKGROUND: Palliative care is nowadays essential in nursing care, due to the increasing number of patients who require attention in final stages of their life. Nurses need to acquire specific knowledge and abilities to provide quality palliative care. Palliative Care Quiz for Nurses is a questionnaire that evaluates their basic knowledge about palliative care. The Palliative Care Quiz for Nurses (PCQN) is useful to evaluate basic knowledge about palliative care, but its adaptation into the Spanish language and the analysis of its effectiveness and utility for Spanish culture is lacking. PURPOSE: To report the adaptation into the Spanish language and the psychometric analysis of the Palliative Care Quiz for Nurses. METHOD: The Palliative Care Quiz for Nurses-Spanish Version (PCQN-SV) was obtained from a process including translation, back-translation, comparison with versions in other languages, revision by experts, and pilot study. Content validity and reliability of questionnaire were analyzed. Difficulty and discrimination indexes of each item were also calculated according to Item Response Theory (IRT). FINDINGS: Adequate internal consistency was found (S-CVI = 0.83); Cronbach's alpha coefficient of 0.67 and KR-20 test result of 0,72 reflected the reliability of PCQN-SV. The questionnaire had a global difficulty index of 0,55, with six items which could be considered as difficult or very difficult, and five items with could be considered easy or very easy. The discrimination indexes of the 20 items, show us that eight items are good or very good while six items are bad to discriminate between good and bad respondents. DISCUSSION: Although in shows internal consistency, reliability and difficulty indexes similar to those obtained by versions of PCQN in other languages, a reformulation of the items with lowest content validity or discrimination indexes and those showing difficulties with their comprehension is an aspect to take into account in order to improve the PCQN-SV. CONCLUSION: The PCQN-SV is a useful Spanish language instrument for measuring Spanish nurses' knowledge in palliative care and it is adequate to establish international comparisons.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".