Narrative-Based Practice
Bibliographic record
Abstract
Narrative-based practice has been developed to bring the health care aspects of illness and treatment closer to the psychosocial and life experiences of a patient. It gives value to the lived experience by using writing tools, spoken words, poetry, drawing, and photography. Nephrology has become one of the first health care fields, likely due to its large patient burden of both critical and chronic disease, to use narrative-based practice. The use of narrative-based practice in renal care explores the lived experience through structured and semistructured interviews with patients, caregivers, and health care providers. The principle topics discussed are the lack of a "disease identity" that would allow patients to identify themselves with a specific state of illness, the "uncertainty" of living with an illness characterized by continuous progression and regression, and the living with the "unspeakable" looming specter of death. This review highlights the powerful significance of qualitative knowledge gained with the narrative method. Increased awareness of these aspects of patients' lived experiences can help nurses improve the quality and effectiveness of the therapeutic relationship between patient and health care professional and may offer a promising approach, within this relationship, to decreasing patient feelings of isolation.
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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 | 0.044 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".