Effects of an educational intervention to strengthen humanistic practice on haemodialysis nurses’ caring attitudes and behaviours and quality of working life: a cluster randomised controlled trial
Bibliographic record
Abstract
Abstract Background. Nurses are trained to establish a trusting relationship with patients in order to create a salutogenic environment. However, in tech-heavy care settings, such as haemodialysis units, dehumanising practices tend to emerge and take root for various reasons to the potential detriment of both patients and nurses. For patients, this may lead to a deterioration of quality of life and, ultimately, of health status. For nurses, it may cause a deterioration of the work environment and, in turn, of quality of working life. Swiss researchers developed, based on Watson’s Theory of Human Caring, and tested, by way of an experimental design, a brief educational intervention for haemodialysis nurses to strengthen their humanistic practice in the aim of improving the nurse-patient relationship and nurse quality of working life.Methods. One hundred and one haemodialysis nurses recruited in ten hospitals in French-speaking Switzerland were randomised into an experimental group that received the intervention and a control group that did not. The nurse-patient relationship was measured with the Caring Nurse-Patient Interaction Scale (EIIP-70) and nurse quality of life at work was measured with the Quality of Work Life Questionnaire at four time points: pre-intervention, intervention completion, and six-month and one-year follow-ups. Random intercept regression analysis was used to evaluate change over time in the two variables under study.Results. The intervention appeared to reinforce nurse attitudes and behaviours geared to a more humanistic practice. The effect seemed to fade over time but was still present one year post-intervention, though limited to certain dimensions of the nurse-patient relationship. Nurse quality of working life, too, seemed positively impacted. The cultural dimension of nurse quality of working life, that is, the degree to which everyday work activities attune with personal and cultural values, recorded the most noticeable improvement.Conclusions. Results support a positive effect of the intervention over both the short term and the medium-to-long term. A brief intervention of the sort may constitute an effective means to improve the nurse-patient relationship by preventing or reducing dehumanising practices.Trial registration: NCT03283891
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".