Bibliographic record
Abstract
Health research grows ever more holistic in its understanding of health and illness, more comprehensive in empirical questions, and more interdisciplinary in approaches. As we investigate social and personal aspects of health, we become drawn to social science knowledge in addition to biomedical and epidemiological perspectives. With this multidisciplinary basis for clinical knowledge comes “qualitative” research, an empirical method seemingly at odds with traditional rules of evidence and with the hierarchy of research designs propounded by evidence-based medicine.1, 2 The philosophy of evidence-based medicine suggests that as ways of knowing, induction is inferior to deduction, subjective perceptions are inferior to objective quantification, and description is inferior to inferential testing. Qualitative tenets invert these imperatives: investigators aim for inductive description using subjective interpretation. New readers of qualitative reports thus confront 3 issues. Firstly, does qualitative inquiry belong at the bottom of evidence-based medicine's traditional research design hierarchy? Secondly, if familiar rules of evidence do not apply, what features distinguish a noteworthy study? Thirdly, what is the clinical usefulness of qualitative research information compared with that of quantitative information? “Qualitative” health research is best characterised not by its qualitative data but by several assumptions about what social reality is like (ontology) and how we can best learn the truth about this reality (epistemology). These premises differ from those required to conduct, analyse, and believe in the results of quantitative research, such as a randomised controlled trial. Quantitative clinical research typically addresses biomedical questions. It tests hypothesised causal relations between quantified variables. (These include, of course, statistically “qualitative” variables, which are those that can be categorised and counted.) Quantitative research questions require key ingredients. Firstly, they require variables that describe natural phenomena coupled with a belief that these variables exist and can be measured objectively. Secondly, they require a belief that …
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.596 | 0.659 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.011 | 0.106 |
| Scholarly communication | 0.043 | 0.066 |
| Open science | 0.013 | 0.041 |
| Research integrity | 0.021 | 0.040 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".