Bibliographic record
Abstract
BACKGROUND: Today, women have an enlightened view towards their life cycles, which is evidence of their healing potential. Women need to share their insights about their healing potential gained through self-reflective processes. Their voices must be heard so that we can benefit from their collective wisdom. The process of healing through self-reflection has begun as a group of nurses share their insights. Documenting the perspectives of these nurses provides the opportunity for other women to learn from and apply this knowledge to their lives. METHOD: Through purposive sampling, eight registered nurses, all women, were selected to participate in in-depth, personal, semi-structured interviews. The purposes in this paper are to describe a three-stage (i.e. awareness, critical analysis, and new perspective) reflective-thinking model and discuss the application of this model to women's expressed inner knowledge and wisdom across personal and professional life cycles. RESULTS: Three themes, signifying their ability to heal themselves, were labelled: Spirituality, Be-ing Versus Do-ing, and Eustress Versus Distress. CONCLUSIONS: Essentially, self-reflection results from both personal and professional stimuli and signifies the need for change so that healing can begin. Recommendations are offered for nurse educators and researchers.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".