Nursing Role Ambiguity in Alberta: The Impact and Institutional Influences
Bibliographic record
Abstract
Increasing health system demands and costs, in an economically strained environment, places extraordinary challenges on Alberta’s workforce planners who continue to address critical gaps. In addition to routine operational planning, unpredictable and often-reactive market demands continuously influence workforce needs. The role and scope of health care providers’, particularly nurses, is constantly evolving which leads to difficulty interpreting their differences. To achieve successful shifts toward team-based collaborative care, alignment of the most appropriate health care provider to patient groups and settings is required. This is challenging when skill sets, and scope are confusing to administrators. Scope changes impact academic programming, regulatory processes and can create confusion and ambiguity for many providers, especially nurses. Role ambiguity among nurses, unabated by key institutions, contributes to inefficiencies and can be potentially harmful to patients. Hence, role ambiguity in nursing creates challenges for employers, educators, regulators, and nurses themselves. Historical reports of role ambiguity pertaining to Alberta nurses do exist however the current state is ambiguous, as are the mitigating strategies. The purpose of this paper is to critically examine the literature that defines role ambiguity, its impact, highlight antecedents and explore the role of key stakeholder institutions best positioned to address the issue in Alberta.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".