A Process to Clarify Roles and Responsibilities of Health Care Professionals
Bibliographic record
Abstract
The need to clarify the roles and responsibilities of health professionals working in our organization arose as a result of changing professional scope of practice regulations, variations in the distribution of specific health care professional groups across the region, and concerns about quality of care. A quality council working group was struck to develop and pilot a role clarification process for endorsement by executives. This report provides information about the process and its application in our organization. First, a term of reference was established to provide a framework for the process. Then, guidelines for users to work through the role clarification process were developed. In applying the guidelines to a specific organizational challenge, the working group enhanced the process by designing a template used to facilitate discussion with the relevant professional groups. The template, a simple cross-functional process map with the patient experience on one axis and high-level generic activities on the other, was used by participants to define discrete functional activities. The template exposes potential areas of conflict, supporting dialogue and allowing further definition of professional responsibilities. The process and tools developed are applicable to any professional scope of practice change or conflict. Utilization of the tools before piloting a change will facilitate the transition, especially when role clarification is a prerequisite. In our organization, it has been successfully used to clarify point-of-care testing roles and responsibilities for laboratory and nursing professionals.
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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 | 0.171 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".