Theory utilization in applied qualitative nursing research
Bibliographic record
Abstract
AIMS: To explore the nuances of theory utilization in qualitative methodologies, discuss the different relationships that applied qualitative methodologies have with theory and use the foundational underpinnings of interpretive description to challenge strongly entrenched ideas of theory that have extended into applied qualitative nursing research. DESIGN: Methodology discussion paper. DATA SOURCES: Narrative literature review and personal observations. CONCLUSION: Many qualitative research traditions have viewed the use of an explicit theoretical framework as an integral grounding for qualitative research studies. Much of the discussion of theory in extant qualitative methodological literature focuses on its use in the context of traditional methodologies such as ethnography, phenomenology and grounded theory, with less attention on methodological approaches developed for applied and practice disciplines such as nursing. Uncritical adoption of ideas about theory based on traditional qualitative methodological conventions can result in findings with little utility for application to the practice context. IMPACT: Nursing researchers should think critically about how theory is used in research endeavours geared towards applied practice and ensure that their methodological choices are in alignment with their philosophical and disciplinary epistemological positionings.
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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 | 0.408 | 0.480 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 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".