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Record W4241648353 · doi:10.29173/jpnep15

Nursing Role Ambiguity in Alberta: The Impact and Institutional Influences

2021· article· en· W4241648353 on OpenAlexfundaboutno aff
Jamie Tycholiz

Bibliographic record

VenueJournal of Practical Nurse Education and Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
FundersAthabasca University
KeywordsAmbiguityNursingPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.559
Teacher spread0.493 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes2
Has abstractyes

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