THE INFLUENCE OF TEAMWORK ON HEALTHCARE WORKERS’ JOB SATISFACTION AND DESIRE TO PROVIDE QUALITY CARE
Bibliographic record
Abstract
Background: Effective teamwork is widely regarded as a means of delivering safe, effective, and patient-centered care and improving patient outcomes, especially when it involves caring for populations with complex health challenges, such as the frail older adults, who often live in long term care institutions. Objective: This aim of this study was to examine how healthcare workers working with older adults perceived teamwork and how teamwork affected care delivery and job satisfaction. Method: Focused ethnographic methods were used to collect data in two residential care settings. Interviews were conducted with 22 healthcare providers who worked in a variety of roles. Results: Characteristics of effective teamwork were: respect, listening, trust, and common goals. Barriers/facilitators of teamwork were: communication, commitment to the work, and familiarity. Perceptions about who was considered a team member varied with narrower views about team membership among healthcare workers providing direct care to older adults. There were expectations that leadership should create an environment that supports teamwork. Moreover, little things like scheduling, role clarification, and working with someone you knew impacted on teamwork experiences. Conclusions: Healthcare workers identified that effective teamwork increased their job satisfaction and commitment to provide better care to older adults; yet, perceptions about who was on the team varied. More research is required to understand how to expand healthcare workers’ perceptions about who they can include as part of the team and how leadership can foster teamwork.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".