Bibliographic record
Abstract
Although many medical and dental journals publish qualitative research this does not mean they are being read by those who could directly benefit from their scholarly contributions. From clinician to the patient. This perspective on qualitative research for medical and dental education was written with the intention of introducing qualitative research to those who may be unaware of its possibilities and utility for clinical education. Its task is to inform others about life conditions they may not have experienced themselves other than in a biomedical context. As researchers, clinicians, and especially for students who read academic, medical, and clinical research papers which are appropriately discipline-and methodology-specific. We may find ourselves encultured to privileging one type of research methodology over others. For example, exclusively considering quantitative research methodologies as being more rigorous and trustworthy. This brief commentary may offer the opportunity for interested healthcare providers and researchers to expand their understanding of the purpose of qualitative research, its role and application in enhancing patient engagement, clinical practices, and person-centered research.
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 | 0.267 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".