<i>When life looks like easy street, there’s danger at your door</i> : Why the respiratory therapy profession should evolve
Bibliographic record
Abstract
Background: Healthcare systems are rapidly transforming in response to demographic pressures, changing funding models, technological advances, and new models of care. As a result, professions must adapt in parallel to remain relevant. In Canada, respiratory therapy stands at an inflection point. Respiratory Therapists (RTs) provide essential and complex care across diverse clinical settings, yet the profession remains anchored in traditional roles. Questions remain about whether RTs are evolving in step with broader system changes, and what may be lost if they do not. Methods: This paper originates from a panel at the 2025 Canadian Society of Respiratory Therapists annual conference. We used narrative methodology and composite narrative techniques to synthesize the reflections of four clinician-researcher panelists into a unified account. Drawing on clinical, professional, policy, and research perspectives, we co-constructed a narrative that highlights shared insights, tensions, and opportunities in the profession. Results: We identified multiple sources of tension. RTs engage with evidence daily, yet most of this evidence comes from other health professions. This reliance constrains the development of RT-specific frameworks, guidelines, and research agendas. Additionally, scholarship in respiratory therapy often remains undervalued and narrowly defined, treated as an optional activity rather than a core part of professional identity. Finally, structural, cultural, and organizational barriers, further restrict the integration of evidence and scholarship into routine work. Discussion: Moving forward requires deliberate action to embed scholarship and evidence generation within the respiratory therapy profession. We suggest that strengthening research literacy at entry-to-practice, creating formal roles for clinical scholars, recognizing scholarly work within career structures and innovation incubators and interprofessional collaborations can position RTs as co-creators of solutions to health system challenges. By embracing these suggestions, the profession can evolve in step with health system change, enhance its influence, and secure its relevance in the future of healthcare.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".