The Respiratory Therapy Practice-Based Outcomes Initiative(RT-PBOI): Developing a framework to explore the value added by respiratory therapists to health care in Alberta
Bibliographic record
Abstract
BACKGROUND: There exists a political imperative to have access to data that meets the needs of health care administrators, governments, and funding bodies to support evidence-informed decision making. It is incumbent upon respiratory therapists to examine how they can deliver the highest-quality patient care, but also that they add value to health systems that ensure the benefits of health innovations are shared equitably among all members of our communities. PURPOSE: To explore the perceived value contributed by the respiratory therapy profession to health care and the health care system in the Province of Alberta at patient, team, and system levels. RESEARCH METHODS: An interpretive descriptive approach was adopted, including the formation of a description and exploration of possible associations, relationships, and patterns within a field of practice. CONCLUSIONS: The qualitative data analysis uncovered a framework that could inform research efforts of the respiratory therapy community in a way that contributes to the proposed mechanisms by which the profession generates value for the organization and patients. The RT-PBOI Conceptual Model identified five key concepts relating to the value contributed by respiratory therapists to health care: technical skills, practice across settings, strategic expertise, tools that leverage capacity, and growing value into the future.
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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 | 0.034 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".