Psychiatrist Health Human Resource Planning – An Essential Component of a Hospital-Based Mental Healthcare System Transformation
Bibliographic record
Abstract
The World Health Organization (WHO) defines health human resource planning as "the process of estimating the number of persons and the kinds of knowledge, skills, and attitudes they need to achieve predetermined health targets and ultimately health status objectives" (OHA 2015). Health human resource planning is a critical component of successful organizational and system transformation, and yet little has been written on how to do this for physicians at the local level. This paper will outline a framework for developing and managing key aspects of physician human resource planning related to both the quantity and quality of work within a hospital setting. Using the example of a complex multiphase hospital-based mental health transformation that involved both the reduction and divestment of beds and services, we will outline how we managed the physician human resource aspects to establish the number of psychiatrists needed and the desired attributes of those psychiatrists, and how we helped an existing workforce transition to meet the new expectations. The paper will describe a process for strategically aligning the selection and management of physicians to meet organizational vision and mandate.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".