The Application of Qualitative Research Findings to Oncology Nursing Practice
Bibliographic record
Abstract
The Oncology Nursing Society (ONS) has established an ambitious research agenda and professional priorities based on a survey by LoBiondo-Wood et al. (2014). With the overall goal to "improve cancer care and the lives of individuals with cancer" (Moore & Badger, 2014, p. 93) through research activities, translating those research findings to direct clinical practice can be overwhelming. As clinicians, understanding how to critique research for quality prior to incorporating research findings into practice is important. The ultimate goal in this critique is to ensure that decisions made about patient care are based on strong evidence. However, the process for appraisal of qualitative research can be ambiguous and often contradictory as a result of the elusive aspect of quality in qualitative research methods (Seale, 1999). In addition, with more than 100 tools available to evaluate qualitative research studies (Higgins & Green, 2011), a lack of consensus exists on how to critically appraise research findings.
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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 | 0.429 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".