Bibliographic record
Abstract
Purpose Traditional academic discourse in qualitative studies is devoid of the subjective individual, and lacks the particulars of experience and the lifelikeness that evokes meaning when researchers address real‐life problems. This paper aims to explore the value and application of utilizing narrative inquiry in nursing research. As a result, this review seeks to argue that understanding the lived experience allows nurse researchers an “insider view” and a deeper understanding of health and social issues that arises from the relationship between the participant and researcher. Additionally this paper aims to highlight some of the challenges and tensions in narrative work including the researcher's self‐reflection within the research process. Design/methodology/approach This paper takes the form of a literature review. Findings This paper highlights some of the challenges and tensions in narrative work including the researcher's self‐reflection within the research process. It argues that understanding the lived experience allows nurse researchers an “insider view” and a deeper understanding of health and social issues that arises from the relationship between the participant and researcher. Originality/value This original article presents an argument that suggests narrative inquiry in nursing research offers a particular way of caring about how knowledge is produced and the importance of the relationship between the researcher and the co‐researcher.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".