Effects on nurses’ quality of working life and on patients’ quality of life of an educational intervention to strengthen humanistic practice among hemodialysis nurses in Switzerland: a protocol for a mixed-methods cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Humanistic nursing practice constitutes the cornerstone of the nursing profession. However, according to some authors, such practice tends to fade over time in favour of non-humanistic behaviours. To contrast this tendency, an educational intervention (EI) based on Watson's Theory of Human Caring was developed and tested in two pilot studies involving, respectively, rehabilitation nurses in Quebec (Canada) and haemodialysis (HD) nurses in Switzerland. In light of the positive results obtained in these, another study is being undertaken to examine more in depth the EI's effects on both HD nurses and patients in French Switzerland. The EI is expected to have positive effects on quality of nurse-patient relationship (NPR), team cohesion, nurse quality of working life (QoWL), and patient quality of life (QoL). METHODS/DESIGN: The study described in this protocol will use a mixed-method cluster randomised controlled trial design. For the quantitative component, nurse and patient data will be collected through questionnaires. The accessible population of 135 nurses and 430 patients will be clustered into 10 HD units. These units will be randomised into an experimental group (EG) and a waiting-list control group (WLCG). Measurements will be taken at baseline (pre-intervention) and repeatedly over time (post-intervention): immediately at EI completion and six and 12 months thereafter. For the qualitative portion of the study, 18 semi-structured interviews will be conducted with EG nurses picked at random two months after EI completion to explore perceived changes in nurse humanistic practice. Qualitative data will be analysed through the relational caring inquiry method, a phenomenological approach. Descriptive and inferential statistics will be computed from the quantitative data. DISCUSSION: The study described in this protocol will determine if and how the proposed EI promotes humanistic nursing practice and how this practice affects quality of NPR, nurse QoWL, and patient QoL. Moreover, it will lay the groundwork for offering the EI to nurses in other healthcare sectors. TRIAL REGISTRATION: This clinical study was registered with ClinicalTrials.gov [NCT03283891, 14/09/2017].
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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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".