A study of deficiencies in teamwork skills among Jordan caregivers
Bibliographic record
Abstract
Purpose The purpose of this paper is to present the deficiencies in teamwork skills at Jordan hospitals as seen by team members. The study aims to identify training needs to improve teamwork-related soft skills of caregivers to enhance staff satisfaction and improve quality of care. Moreover, the paper provides a methodology to identify the training needs in any healthcare workplace by repeating the same questionnaire. Design/methodology/approach A self-administrated questionnaire was designed to study deficiencies in teamwork and team leadership at Jordan hospitals as seen by team members. Surveyed care providers included physicians, nursing and anesthesiologists operating in emergency departments, surgical operating rooms and intensive care units from various hospitals. Findings With a response rate of 78.8 percent, statistical analysis of collected data of opposing staff members revealed low levels of satisfaction (40.7-48 percent opposing), lack of awareness on the impact of teamwork on quality of care (15.6-22.1 percent opposing), low levels of involvement of top management (27.1-57.3 percent opposing), lack of training (52.5-69.8 percent opposing), lack of leadership skills (29.8-60 percent opposing), lack of communication (22.3-62.1 percent opposing), lack of employee involvement (37.6-50.8 percent opposing) and lack of collaboration among team members (28.6-50 percent opposing). Among the many, results illustrate the need for improving leadership skills of team leaders, improving communication and involving team members in decision making. Originality/value Several studies investigated relationships between teamwork skills and quality of care in many countries. To the authors' knowledge, no local study investigated the deficiencies of teamwork skills among Jordan caregivers and its impact on quality of care. The study provides the ground for management at Jordan hospitals and to healthcare academic departments to tailor training courses to improve teamwork skills of caregivers. Data of this study are collected from the society who is working in the field of healthcare. As the results of this are produced from a real data, it is expected that applying the recommendations will impact the society positively by enhancing the patients' satisfaction.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".