Supporting and Empowering Nurses Undergoing Critical Care Certification
Bibliographic record
Abstract
PURPOSE: Nurses working in critical care settings face multiple sources of stress, such as complex clinical situations and the use of new advanced technologies, which can affect their psychological health. Literature suggests that the promotion of educational activities, such as a certification process within a specialty, can contribute to nurses' empowerment, professional growth, and personal satisfaction. However, it is of utmost importance that the institutional organizations support nurses undergoing the certification process to optimize positive impacts of this educational activity on the nurses, on the patients, and within the institutions. DESCRIPTION OF THE PROJECT: In the course of a graduate studies stage, an educational program aiming at supporting and creating an empowering environment for nurses undergoing a critical care certification process was developed and implemented, in a Canadian intensive care unit, over a 7-month period. The Humanist model was used as a theoretical framework and was complemented by the problem-based learning pedagogical approach. OUTCOMES: A postintervention qualitative questionnaire revealed that the program was tailored to nurses' learning needs and that participants felt supported by their institution throughout the process. CONCLUSION: This program could help institutions support nurses in achieving higher professional and personal development levels through specialty certification.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".