The Development of a Mentoring Program for a Local Public Health Agency
Bibliographic record
Abstract
Mentoring programs have many advantages for the agency, mentor, and protégé. The \nfocus of this project is a Local Public Health Agency [LPHA; pseudonym] that is experiencing the attrition of many top executives. The purpose of this project was to develop a plan for implementation of a formal, organization-wide mentorship program for the LPHA. Appreciative Inquiry is recommended as an organizational development (OD) tool to design the mentorship program. Appreciative Inquiry is a positive approach to organizational development that seeks \nout success stories to build a positive future reality. In addition, organizational readiness is assessed to determine the extent which the agency is ready to implement a new program. A program planning framework is described as a means to develop the mentoring program. The framework provides step-by-step guidance for program development to ensure important steps \nare not overlooked that could jeopardize the success of the program. Finally, Adaptive \nMentorship® is recommended as a framework for designing the mentoring program. Adaptive \nMentorship is based on the premise that the mentor adapts his or her mentoring behaviour to match that developmental level of the protégé.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".