A Cross-Sectional Survey of Practice Patterns and Selected Demographics of Respiratory Therapists in India
Bibliographic record
Abstract
BACKGROUND: Respiratory therapy was introduced to India in 1995. Respiratory therapists (RTs) work alongside doctors in hospitals. Of the 993 universities in India, a few have bachelor's or master's programs in respiratory therapy, but no studies have examined the demographics, geographical spread, or skills used by these RTs. This study assessed the demographics and services offered by RTs in India. METHODS: This was a cross-sectional study based on a survey administered on paper, by telephone, or online. RTs were selected by convenience sampling from institutional databases and from WhatsApp groups of RTs in India, as well through snowball sampling of co-workers. A link to the online survey was shared on the author's personal social media channels. Of the invited RTs, 465 consented and participated; of those, 237 answered all questions. RESULTS: Of the 237 respondents completing the survey, 73% had bachelor's degree, 16.5% had a master's degree, 4.6% had a diploma, 2.5% had mixed qualifications, 1.7% had post graduate diploma, 0.8% had a certificate, 0.4% had a master of business administration degree, and 0.4% had a PhD degree. Almost all (96.6%) worked as an RT or in a job that required respiratory therapy knowledge. Although individuals may have had multiple job roles, 77.6% worked as a hospital staff RT. The least frequently performed competencies were recommending diagnostic procedures, using evidence-based principles, initiating and conducting patient and family education, and administering home care and pulmonary rehabilitation; the most frequently performed competencies were support oxygenation and ventilation, ensuring infection control, and maintaining a patent airway. CONCLUSIONS: Most subjects were employed in south India and had a bachelor's degree. They worked as staff RTs with a focus on the acute care environment. Pneumonia, asthma, COPD, and ARDS were the most commonly managed diseases. Competencies such as recommending procedures, planning and providing pulmonary rehabilitation, and administering home-based care were the least frequently performed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".