A survey on the attitudinal differences between acute and community settings
Bibliographic record
Abstract
Introduction: While challenges facing community and acute care practitioners have been studied elsewhere, this is not the case for respiratory therapists (RTs). This study aimed to examine attitudinal differences amongst RTs in British Columbia regarding challenges faced by acute and community settings. Methods: A 40-item anonymous online survey was sent to members of the British Columbia Society or Respiratory Therapists. Of the 40 questions, 11 were relevant to the study's aim. Results: ≤ 0.05) based on work setting. Acute care had the highest percentage of responses for challenges related to technology, stress, inter-professional collaboration, and training. Community settings had the highest percentage in challenges related to independence and education. Both being equal received the highest percentage in challenges related to problem-solving, interpersonal, communication, and resource management. Discussion: While attitudinal differences exist, they are not extreme. It did not appear that respondents' primary motivation was to vote along "party lines". Conclusions: The setting an RT works in can influence attitudes related to stress and interpersonal challenges. Despite this, one setting is not universally more challenging. Acute care settings can have greater technological, inter-professional, and training-related challenges. Community settings can have greater independence and education-related challenges. Both settings can provide similar challenges with problem-solving, communication, and resource management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".