Practice guidelines as multipurpose tools: A qualitative study of noninvasive ventilation*
Bibliographic record
Abstract
OBJECTIVE: Although practice guidelines for noninvasive ventilation (NIV) for patients with acute respiratory failure (ARF) have the potential to improve processes of care and patient outcomes, clinicians' views about life support technology guidelines are not well understood. The objective was to understand the knowledge about and attitudes toward an NIV guideline for patients with ARF and potential barriers to its use. DESIGN: Qualitative study based on individual, in-depth, semistructured interviews. SETTING: St. Joseph's Healthcare, Hamilton, Ontario. SUBJECTS: Thirty clinicians (six attending physicians, five residents, 12 nurses, and seven respiratory therapists) who used NIV for chronic obstructive pulmonary disease and congestive heart failure patients with ARF, before and after NIV guideline implementation. INTERVENTIONS: We elicited knowledge and attitudes about, behaviors toward, and barriers to our institutional NIV guideline. We transcribed all interviews and analyzed data in triplicate using grounded theory to identify themes and develop a framework for understanding clinicians' views on guidelines. MEASUREMENTS AND MAIN RESULTS: The NIV guideline was perceived to define individual clinical responsibilities, improve clinician comfort with use of technology, increase patient safety, and reduce practice variability. Barriers to guideline use included lack of awareness of the guideline, unclear guideline format and presentation, and reluctance about changing practice. Contrary to previous research, participants in this study did not report that the practice guideline limited clinical autonomy. Clinicians used the guideline variously as an educational resource, to access monitored beds, to avoid clinical conflict, or to leverage professional credibility. CONCLUSIONS: This qualitative study illustrated how the NIV guideline at our institution is understood as a tool that facilitates the multidisciplinary care of patients with ARF. Guideline use may be enhanced through education to improve guideline awareness and increase comfort with recommended practices. Developers should be aware of the role of guidelines for purposes other than bedside decision making for individual patients.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".