A moral profession: Nurse educators’ selected narratives of care and compassion
Bibliographic record
Abstract
Abstract \nBackground: Lack of compassion is claimed to result in poor and sometimes harmful nursing care. \nDeveloping strategies to encourage compassionate caring behaviours are important because there is \nevidence to suggest a connection between having a moral orientation such as compassion and resulting \ncaring behaviour in practice. \nObjective: This study aimed to articulate a clearer understanding of compassionate caring via nurse \neducators’ selection and use of published texts and film. \nMethodology: This study employed discourse analysis. \nParticipants and research context: A total of 41 nurse educators working in universities in the United \nKingdom (n ¼ 3), Ireland (n ¼ 1) and Canada (n ¼ 1) completed questionnaires on the narratives that \nshaped their understanding of care and compassion. \nFindings: The desire to understand others and how to care compassionately characterised educators’ \nchoices. Most narratives were examples of kindness and compassion. A total of 17 emphasised the \nimportance of connecting with others as a central component of compassionate caring, 10 identified the \nburden of caring, 24 identified themes of abandonment and of failure to see the suffering person and \n15 narratives showed a discourse of only showing compassion to those ‘deserving’ often understood as \nthe suffering person doing enough to help themselves \nDiscussion: These findings are mostly consistent with work in moral philosophy emphasising the particular \nor context and perception or vision as well as the necessity of emotions. The narratives themselves are used \nby nurse educators to help explicate examples of caring and compassion (or its lack). \nConclusion: To feel cared about people need to feel ‘visible’ as though they matter. Nurses need to be \nalert to problems that may arise if their ‘moral vision’ is influenced by ideas of desert and how much the \npatient is doing to help himself or herself.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".