Les caractéristiques des tuteurs de résilience des étudiants en soins infirmiers vulnérabilisés
Bibliographic record
Abstract
Nursing training seems to make students vulnerable to stress or burnout. Nevertheless, the majority succeeded in this training. This positive recovery despite a deleterious context of study questions about this schooling, and on possible resilient mechanisms and tutors of resilience. This research paper in educational sciences will begin with a synthesis of the results of publications about stressors and risk's factors of burnout of these students. We will see how this schooling can be linked to the concept of vulnerability and resilience. Then, we will present the results and the thematic analysis of 30 semi-directive interviews. The objectives of those ones were: to check factors vulnerability of this training, to determine if resilient processes can be observed, and to identify the characteristics of the resilience tutors of these weakened students. After the presentation of the results and of the analysis, we will discuss the links between vulnerability, post-traumatic stress disorder and burnout. We will explain the concept of compassion as one of the predominant characteristics of tutors. Finally, concerning the relational posture of education's professionals, we will show how they could professionally support students' resilience.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".