Bibliographic record
Abstract
AIM: To report an analysis of the concept of the unsayable. BACKGROUND: Within nursing, there is recognition that not all experiences of illness can be fully voiced and therefore may be unsayable. However, focus has been on that which is sayable, those experiences that can be communicated through language, leaving the unsayable unexamined. There is little examination of the meaning or relevance of the concept for nursing practice. DATA SOURCES: The literature search was not limited by date and includes English, peer-reviewed texts in the databases CINAHL, Web of Science, and PsychINFO from 1959-2011. DESIGN: Rodgers' method of evolutionary concept analysis was used. REVIEW METHODS: References were read and analyzed according to surrogate terms, related concepts, attributes, antecedents, and consequences. RESULTS: Three surrogate terms, one related concept, four attributes, four antecedents, and two consequences were identified in this concept analysis. Based on this analysis, the unsayable refers to what is not expressed yet alluded to through language and may be conscious or unconscious. The meaning of this concept differs substantially between psychology and nursing. CONCLUSION: Although literature on the unsayable has been developed primarily outside the discipline of nursing, exploration of the concept within nursing may assist nurses to consider situations and experiences that are challenging, elusive, and perhaps impossible for patients to language while living amid illness.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".