Job Satisfaction of Registered Respiratory Therapists in Primary Care: Addressing Recruitment and Retention in Ontario and Manitoba
Bibliographic record
Abstract
Registered Respiratory Therapists (RRTs) have unique skills in managing chronic obstructive pulmonary disease (COPD) in primary care settings. With an 82% increase in COPD diagnoses between 2000 and 2010 in Canada, the fact that over 10% of Canadians aged 35 and older are living with COPD, and primary care reform in Ontario and Manitoba, we would expect an increasing number of RRTs working in this setting. However, this is not happening. Through the concept of job satisfaction, we want to investigate the barriers of integrating this allied healthcare professional into primary care settings. Using a pragmatic approach, we examined RRT job satisfaction in primary care via semi-structured interviews of 19 RRTs in Manitoba and Ontario in 2018 and 2019. A combined inductive and deductive (Mottaz framework) analysis approach allowed us to cross reference work context with job satisfaction. The context in which primary care is operationalized impacts RRT job satisfaction. In Ontario, retention of RRTs in primary care was the main issue due to lower salaries and benefits. In Manitoba, recruitment of RRTs in primary care was the main issue due to lack of human resources and funding. Efforts should be made to address gaps in job satisfaction of RRTs in primary care. To improve retention in Ontario, RRTs should be compensated similarly to their counterparts in acute care. In Manitoba, there should be increased positions for RRTs in primary care. Developing strategies for enhancing job satisfaction will ensure the delivery of high-quality, patient-centered care. This study provides both theoretical and practical contributions to primary care workforce research. Theoretically, our findings demonstrate how contextual factors moderate job satisfaction, showing that the primary care context produces various work situations and heavily impacts work satisfaction. Practically, our results offer specific guidance for healthcare policymakers and administrators, including standardizing compensation across care settings, converting part-time positions to full-time roles, and developing targeted educational initiatives to improve workforce recruitment and retention in underserved areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".