Use of a qualitative methodological scaffolding process to design robust interprofessional studies
Bibliographic record
Abstract
Increasingly, researchers are using qualitative methodology to study interprofessional collaboration (IPC). With this increase in use, there seems to be an appreciation for how qualitative studies allow us to understand the unique individual or group experience in more detail and form a basis for policy change and innovative interventions. Furthermore, there is an increased understanding of the potential of studying new or emerging phenomena qualitatively to inform further large-scale studies. Although there is a current trend toward greater acceptance of the value of qualitative studies describing the experiences of IPC, these studies are mostly descriptive in nature. Applying a process suggested by Crotty (1998) may encourage researchers to consider the value in situating research questions within a broader theoretical framework that will inform the overall research approach including methodology and methods. This paper describes the application of a process to a research project and then illustrates how this process encouraged iterative cycles of thinking and doing. The authors describe each step of the process, shares decision-making points, as well as suggests an additional step to the process. Applying this approach to selecting data collection methods may serve to guide and support the qualitative researcher in creating a well-designed study approach.
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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 | 0.362 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".