Elisions in the field of caring
Bibliographic record
Abstract
BACKGROUND: Contemporary research into caring in nursing was criticized in the pages of this journal by John Paley. He charged that the study of caring has not been advanced by research which, he reckoned, merely generates endless lists of terms to describe caring. He also argued that research in the field was largely flawed by confusion over the difference between things said about caring and the act of caring itself. THE PRESENT PAPER: We have analysed Paley's criticism. Essentially, he is criticizing the whole field of survey research. The scientific process is underpinned by the implicit understanding that any field moves forward cautiously. In the social sciences multiple perspectives enrich understanding of phenomena and often confirm previous perceptions. The lack of any alternative approach from Paley is evident. Examples from psychology, where seemingly endless lists of descriptors have led through rigorous concept and statistical analysis to genuinely useful psychological and clinical data, are expounded. In contrast to Paley's assertions, the study of caring in nursing to date has also produced information which is useful within nurse education and practice. CONCLUSION: There is no confusion concerning the things said about and the things done in the name of caring in our minds. We acknowledge that studying the actual phenomena of caring is difficult. However, in the absence of definitive descriptions of caring and precise methods to study it, the search for perfection has not paralysed action. Much has been learned about caring and much remains to be learned.
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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 | 0.030 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.095 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".