Validation d’une formation en soutien à l’exercice de la fonction d’assistante infirmière-chef : une méthode Delphi
Bibliographic record
Abstract
Background: A charge nurse holds a clinical-administrative management position in health care institutions. To perform this position effectively, they must demonstrate specific competencies, including leadership, interpersonal communication, clinical-administrative caring, problem-solving, and knowledge and understanding of the work environment. Due in part to the shortcomings of existing training targeting this position, charge nurses receive limited support in terms of the appropriation of their position and the strengthening of their competencies. In response to this situation, a training was developed that takes into consideration the five required competencies, includes principles that promote the transfer of training, and would be systematically provided to new charge nurses exercising their position in health care institutions. This training was then subjected to an empirical validation process in order to be scientifically recognized. Objective: To empirically validate the training developed to support charge nurses in fulfilling their position, by assessing content validity and reliability. Method: This study is based on the conceptual model entitled “Modèle de formation pour l’assistante infirmière-chef” as well as the methodological framework Conducting and REporting DElphi Studies (CREDES) and reporting guidelines established by Spranger et al. (2022). A Delphi approach was used to validate the training by through a self-administered online questionnaire completed by experts. The questionnaire was divided into different sections, each containing specific validation questions. Content validity and reliability were assessed. A content validity index (CVI) ≥ 0.80 was targeted for each section and for each individual questions within those sections. A content analysis of the qualitative data obtained from the expert comments was conducted; this was followed by a non-parametric Mann–Whitney test for each section to assess the training's reliability. Findings: Two consultation rounds proved necessary. A total of 33 experts participated in the first round, and 21 in the second. Regarding content validity, after the second round of consultations, 11 of 12 sections and 104 of 113 questions had a CVI ≥ 0.80. The qualitative data analysis led to modifications in the training content for sections and questions that did not achieve the target validity index, in addition to generating seven recommendations to be considered when developing a training. Lastly, reliability was present with a p > 0.05 for nine out of 12 sections. Conclusion: This study validated the training developed to support charge nurses in fulfilling their position by strengthening their competencies. Supporting charge nurses in this manner will have positive impacts, particularly on the quality of care, the safety of users, and the retention rate of nursing staff.
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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 | 0.111 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".