Teamwork and Patient Care Teams in an Acute Care Hospital
Bibliographic record
Abstract
The literature suggests that effective teamwork among patient care teams can positively impact work environment, job satisfaction and quality of patient care. The purpose of this study was to determine the perceived level of nursing teamwork by registered nurses, registered practical nurses, personal support workers and unit clerks working on patient care teams in one acute care hospital in northern Ontario, Canada, and to determine if a relationship exists between the staff scores on the Nursing Teamwork Survey (NTS) and participant perception of adequate staffing. Using a descriptive cross-sectional research design, 600 staff members were invited to complete the NTS and a 33% response rate was achieved (N=200). The participants from the critical care unit reported the highest scores on the NTS, whereas participants from the inpatient surgical (IPS) unit reported the lowest scores. Participants from the IPS unit also reported having less experience, being younger, having less satisfaction in their current position and having a higher intention to leave. A high rate of intention to leave in the next year was found among all participants. No statistically significant correlation was found between overall scores on the NTS and the perception of adequate staffing. Strategies to increase teamwork, such as staff education, among patient care teams may positively influence job satisfaction and patient care on patient care units.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".