Overcoming Obstacles to Develop High-Performance Teams Involving Physician in Health Care Organizations
Bibliographic record
Abstract
Many health care organizations struggle and often do not succeed to be high-performance organizations that are not only efficient and effective but also enjoyable places to work. This review focuses on the physician and organizational roles in limiting achievement of a high-performance team in health care organizations. Ten dimensions were constructed and a number of competencies and metrics were highlighted to overcome the failures to: (i) Ensure that the goals, purpose, mission and vision are clearly defined; (ii) establish a supportive organizational structure that encourages high performance of teams; (iii) ensure outstanding physician leadership, performance, goal attainment; and (iv) recognize that medical team leaders are vulnerable to the abuses of personal power or may create a culture of intimidation/fear and a toxic work culture; (v) select a good team and team members-team members who like to work in teams or are willing and able to learn how to work in a team and ensure a well-balanced team composition; (vi) establish optimal team composition, individual roles and dynamics, and clear roles for members of the team; (vii) establish psychological safe environment for team members; (viii) address and resolve interpersonal conflicts in teams; (xi) ensure good health and well-being of the medical staff; (x) ensure physician engagement with the organization. Addressing each of these dimensions with the specific solutions outlined should overcome the constraints to achieving high-performance teams for physicians in health care organizations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".