De la clinique à la recherche : l’auto-ethnographie comme outil d’analyse des transitions identitaires du chercheur en sciences infirmières
Bibliographic record
Abstract
The scientific legitimacy of nursing research depends on its adherence to different scientific criteria. Despite the lack of consensus on predetermined criteria, reflexivity is widely discussed as a strategy to establish rigour in qualitative research. Unfortunately, with the exception of tools such as the reflexive journal, little is said about how reflexivity can be completed. Several recent studies suggest the relevance of autoethnography to support the reflexive approach of nurse researchers. Inspired by the findings of an autoethnography and a narrative literature review, this article examines how this approach can contribute to the reflexivity of the nurse researcher. Autoethnography seems particularly adapted to the professional and academic context in which many nurses evolve. As a reflective tool, autoethnography can promote the development of the researcher's self-awareness, provide analytical tools to help better understand the influence of previous experiences on the relationship to research and report on the transition between different professional identities. In order to contribute to the debate on the use of autoethnography in qualitative research, central elements to this approach are discussed.
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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 | 0.059 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".