A dimensional structure of nurse–patient interactions from a caring perspective: refinement of the Caring Nurse–Patient Interaction Scale (CNPI‐Short Scale)
Bibliographic record
Abstract
AIM: This paper reports the development of a short version of the Caring Nurse-Patient Interaction Scale. BACKGROUND: Since the 1980s several instruments have been developed to assess external aspects of caring. They involve using an inductive process of knowledge development to investigate the underlying structure of caring, and few reflect an explicit underlying caring theory. We developed the Caring Nurse-Patient Interactions Scale (CNPI-Long Scale) based on both inductive and deductive processes to assess attitudes and behaviours associated with Watson's 10 carative factors. Two issues led us to abridge our original 70-item scale into a more concise Short Scale (CNPI-Short Scale). First, many of our subscales were moderately to highly correlated, which is an empirical reflection of the theoretical non-independence of the carative factors. Secondly, a 70-item questionnaire was difficult to be deal with in the clinical research setting with severely ill patients because of its length. METHOD: Items selected were determined by factor analysis, with specific theoretical and empirical requirements. Data were collected in September 2003 from 377 nursing students beginning their first, second or third year of a nursing programme. RESULTS: The Short Scale comprises 23 items, reflecting four caring domains: Humanistic Care (four items), Relational Care (seven), Clinical Care (nine) and Comforting Care (three). All items are related to their theoretical domain alone (i.e. factor loading >or=0.40). Alpha coefficients for the four domains were adequate (0.63-0.74, 0.90-0.92, 0.80-0.94 and 0.61-0.76 respectively). CONCLUSIONS: The CNPI-Short Scale, has potential for use in clinical research settings, particularly when questionnaire length is an issue. It is a useful tool for research aimed at demonstrating that caring is indeed fundamental to nursing.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".