Bibliographic record
Abstract
PURPOSE: The author is a nursing management practitioner, whose purpose in writing this paper is twofold: to examine the impact of corporate memory loss on a health care institution, caused by increasing retirement rates of senior executives; and to use this research as an opportunity for action learning where both the author and the institution can benefit from the learning outcomes. DESIGN/METHODOLOGY/APPROACH: Using qualitative research methods based on ethnographic interviewing techniques and grounded theory, the author interviews 12 senior executives from four diverse health care facilities. The purpose is to determine the point at which corporate memory loss, in the form of tacit knowledge in the heads of departing executives, becomes a problem for the institution. FINDINGS: The research determined that the requisite managerial competencies normally assumed for senior management positions are insufficient to minimize the negative impacts of corporate memory loss caused by departing senior executives. Effective knowledge management and knowledge transfer within the organization are fundamental for ongoing organizational effectiveness. RESEARCH LIMITATIONS/IMPLICATIONS: The research is limited to 12 senior executives. The grounded theory nature of the research provides a framework for more research in other institutions to test and further explore some of the findings. PRACTICAL IMPLICATIONS: One of the most significant threats facing the majority of health care organizations related to the aging workforce is the greater number of staff who are retiring from all levels within the organization. The development of techniques to reducing the impact of corporate memory loss on the culture of an organization will increase its effectiveness, help build continuity, and provide a more secure footing for the workforce of the future. ORIGINALITY/VALUE: The exit of knowledge workers is causing a major problem for Canada's health care organizations. This study throws more light on to this problem from the point of view of senior executives who have been specifically impacted by the problem of corporate memory loss.
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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".