Improving medical personnel selection and appointment processes
Bibliographic record
Abstract
PURPOSE: This paper seeks to argue that processes for selecting and appointing medically qualified personnel in some healthcare organizations may be limited, especially those that emphasize qualifications rather than expanding the criteria to include practice scope, person-organization fit and capability to function within a healthcare team. DESIGN/METHODOLOGY/APPROACH: The paper is based on the authors' experiences and a literature review. FINDINGS: Selection based purely on academic merit, advanced clinical training, skills and professional achievements may not address other essential selection criteria. Medical personnel need to possess competencies such as ability to give high quality care and work constructively in a clinical team; communication skills; willingness to actively participate in quality and safety programs; teaching ability; management and leadership skills; and support institutional values and corporate aims. These attributes are often over-looked and cannot be assumed from academic merit and achievements. RESEARCH LIMITATIONS/IMPLICATIONS: The study's conclusions are based on the authors' experiences and literature review. Future studies may wish to examine selection technique efficacy and outcomes empirically. PRACTICAL IMPLICATIONS: Better medical personnel selection and appointment processes are likely to reduce unnecessary costs associated with poorly-made appointments, improve patient outcomes and may have a formative role encouraging medical personnel to take a broader view of their healthcare organization roles. ORIGINALITY/VALUE: The authors challenge selection panel members to consider non-traditional with normal selection criteria for medical appointments. Nine recommendations for enhancing selection processes are provided.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".