Utility of Qualitative Research Findings in Evidence‐Based Public Health Practice
Bibliographic record
Abstract
Epidemiological data, derived from quantitative studies, provide important information about the causes, prevalence, risk correlates, treatment and prevention of diseases, and health issues at a population level. However, public health issues are complex in nature and quantitative research findings are insufficient to support practitioners and administrators in making evidence-informed decisions. Upshur's Synthetic Model of Evidence (2001) situates qualitative research findings as a credible source of evidence for public health practice. This article answers the following questions: (1) where does qualitative research fit within the paradigm of evidence-based practice and (2) how can qualitative research be used by public health professionals? Strategies for using qualitative research findings instrumentally, conceptually, and symbolically are identified by applying Estabrooks' (1999) conceptual structure of research utilization. Different research utilization strategies are illustrated through the use of research examples from the field of work on intimate partner violence against women. Recommendations for qualitative researchers disseminating findings and for public health practitioners/policy makers considering the use of qualitative findings as evidence to inform decisions are provided.
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 | 0.524 | 0.600 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| 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".