Remaining in the nursing profession: The relevance of strong evaluations
Bibliographic record
Abstract
BACKGROUND:: Why nurses remain in the profession is a complex question. However, strong values can be grounds for their remaining, meaning nurses evaluate the qualitative worth of different desires and distinguish between senses of what is a good life. RESEARCH QUESTION:: The overall aim is to explore and argue the relevance of strong evaluations for remaining in the nursing profession. RESEARCH DESIGN:: This theoretical article based on a hermeneutical approach introduces the concept strong evaluations as described by the Canadian philosopher Charles Taylor and provides examples of nurses' experiences in everyday nursing care drawn from a Norwegian empirical study. PARTICIPANTS AND RESEARCH CONTEXT:: Data collected in the original study consisted of qualitative interviews and qualitative follow-up interviews with 13 nurses. The research context was the primary and secondary somatic and psychiatric health service, inside as well as outside institutions. ETHICAL CONSIDERATION:: The article uses data from an original empirical study approved by the Norwegian Social Science Data Services. Information was given and consent obtained from the participants. FINDINGS:: Remaining in the nursing profession can be understood as revolving around being a strong evaluator. This has been concretized in issues of being aware of different incidents in life and having capacities as a nurse. DISCUSSION:: Why nurses remain is discussed in relation to how nurses have shaped themselves by reflecting on what is of significance in their life. However, being a strong evaluator cannot be seen as the casual condition for remaining. CONCLUSION:: Remaining in the nursing profession is obviously not a contingent matter, rather it is a matter concerned with the qualitative worth of different desires and values. Nurses' awareness of a life choice impacts on whether they remain or not. Consequently, nurses may need to articulate and reflect on their priorities for remaining.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".