Describing the Essential Elements of a Professional Practice Structure
Bibliographic record
Abstract
The proliferation of program management coupled with the Introduction of the Regulated Health Professions Act, prompted many healthcare organizations in Ontario to introduce professional practice models. In addition, the Magnet Hospitals research (Kramer and Schmalenberg 1988) identified the existence of a professional practice model as a key element for recruitment and retention of professional staff. Professional practice models were introduced to address issues of accountability, identity and overlapping scopes of practice as experienced by healthcare professionals and organizations across the continuum of care. The authors of this paper describe exploratory work done through the Professional Practice Network of Ontario to identify the essential elements of the "ideal" professional practice structure, key areas of challenge and strategies for adapting these elements into an organization. The paper presents a list of 16 essential elements of an ideal professional practice structure with a further discussion on four key areas consistently identified as areas of challenge. This paper is intended to report, not the findings of a formal research study, but rather the result of facilitated dialogue among professional practice leaders in Ontario. The information will be of interest to healthcare organizations across the continuum of care and to professional associations and academic institutions, as we all address the challenges of creating a quality work environment that supports and fosters excellence in professional practice.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".