Executive development in healthcare during times of turbulence
Bibliographic record
Abstract
In this research we report an analysis of comments from managers and executives in healthcare organizations to provide insights into the strategic management needs of healthcare organizations. The comments were obtained as part of a survey that asked upper-level managers and executives to identify strategic management skill and knowledge needs in healthcare organizations. After completing the survey, the respondents were given the opportunity to comment on any topics of concern to them. A total of 67 comments, many of them extensive and insightful, were obtained. In this paper, we review the literature dealing with educational and developmental needs of healthcare managers. Much of this literature is academic in nature and permits an interesting comparison to the perspective of management and executive practitioners. Emerging from the literature was a concern for environmental turbulence and a recognition that healthcare managers are at risk of falling behind in terms of skill development under such conditions. Respondent comments suggested a recognition of the potential problems. The comments are classified into four major categories: needs and skills in turbulent conditions; program and educational needs; issue clarification; and additional comments. Moreover, the first two categories appeared to break out into a set of six additional themes, which we suggest will be important to those designing programs for executive development in healthcare during turbulent times. While the source of this research is healthcare settings in Canada and the USA, the findings should be applicable to international healthcare organizations that use strategic management concepts and practices.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".