Motivators and Stressors for Canadian Research Coordinators in Critical Care: The MOTIVATE Survey
Bibliographic record
Abstract
BACKGROUND: Critical care research coordinators implement study protocols in intensive care units, yet little is known about their experiences. OBJECTIVE: To identify the responsibilities, stressors, motivators, and job satisfaction of critical care research coordinators in Canada. METHODS: Responses to a self-administered survey were collected in order to identify and understand factors that motivate and stress research coordinators and enhance their job satisfaction. Items were generated in 5 domains (demographics, job responsibilities, stressors, motivators, and satisfaction). Face validity pretesting was conducted and clinical sensibility was evaluated. Items were rated on 5-point Likert scales. Descriptive analyses were used to report results. RESULTS: The response rate was 78% (66 of 85). Most critical care research coordinators (71%) were employed full time; they were engaged in 9 studies (7 academic, 2 industry); and 49% were nurses. Of 30 work responsibilities, the most frequently cited were submitting ethics applications (89%), performing data entry (89%), and attending meetings (87%). Highest-rated stressors were unrealistic workload and weekend/holiday screening; highest-rated motivators were a positive work environment and team spirit. Overall, 26% were "very satisfied" and 53% were "satisfied" with their jobs. CONCLUSIONS: Critical care research coordinators in Canada indicate that, despite significant work responsibilities, they are satisfied with their jobs thanks to positive work environments and team spirit.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".