Bibliographic record
Abstract
Background Many nurse researchers have embraced narrative methods because of the power of the stories they produce. Narrative enquiry relies on stories for data. Stories are the tools with which people build a sense of their experience of the world and the vehicle by which they share that sense with others. In narrative research, it is essential to follow themes beyond individual stories through to analysis at the social level. The researcher must understand the significance of the narrative for others and for society. Narrative lends itself particularly well to studies of changes in a society and societal groups. Aim To define and describe approaches to narrative enquiry, address their uses and usefulness, and identify potential issues. Discussion Narrative methods require the development of trust. Researchers and participants must understand their roles as well as the processes and intent of the research. While narrative approaches provide powerful stories through which issues can be understood, they must be approached with careful consideration of the power imbalance in the relationship, the vulnerability of the participant and the potential misuse of empathy in influencing outcomes. Conclusion Narrative enquiry provides powerful data to answer important research questions meaningfully. Researchers using narrative enquiry must be mindful of the power of the bond formed in the sharing of stories and not encourage participants to share more than they intend. Implications for practice Nurse researchers need to understand their role as researcher in their relationships with participants and view the powerful stories they are told as a way of answering research questions, not as a call to engage therapeutically to solve problems.
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 | 0.134 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.134 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".