Qualitative methodologies in health research: interpretive referential of Patricia Benner.
Bibliographic record
Abstract
OBJECTIVE: this article reports on the experience of using the interpretive phenomenological framework of Patricia Benner in a Brazilian context. Benner's interpretive phenomenology, based on existential and interpretative philosophy, aims to understand human experiences in the particular worlds of research participants. Data were collected through interviews with nine nurses in November and December 2014. RESULTS: data analysis process according to Benner's framework consisted of: transcription, coding, thematic analysis, and search for paradigmatic cases and examples. Therefore, the prior knowledge of the researcher is an important part of the study, consisting in manners of the research conduction. CONCLUSION: The use of this methodological framework entailed a great challenge for the researcher, however, it also enabled a unique opportunity to illuminate important existential phenomena related to the daily lives of research participants.
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 | 0.051 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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".