Perceptions of team effectiveness following the introduction of a cardiology acute care nurse practitioner role
Bibliographic record
Abstract
Background: Cardiology acute care nurse practitioners (ACNP) enacting their roles in health care teams faced a number of challenges including a mix of positive and negative views of the ACNP role from health care team members, ACNP roles crossing the boundaries between the medical and nursing professions, and an expanded scope of practice that was not clearly defined. Understanding the process by which ACNP roles were enacted in teams is important since the boundaries between professions may change following the introduction of an ACNP role and affect the team's perceptions of its effectiveness. Little is known of how ACNP role enactment affects the team's perceptions of its effectiveness. Objectives: Following the introduction of a cardiology ACNP role in the health care team, the study aims to: 1) describe ACNP role components, 2) describe ACNP role enactment, and 3) explore how ACNP role enactment and boundary work of team members affect the team's perceptions of team effectiveness. Methods: The study was conducted in two university-affiliated teaching hospitals in Quebec. A descriptive multiple case study design with qualitative and quantitative data sources was used. The study produced a valid and reliable time and motion tool to measure cardiology ACNP activities. The study used Bales' interaction process analysis to identify differences in patterns of communication among team members. Findings: Team members believed they were more effective following the introduction of an ACNP role because the ACNP role filled a gap in patient care and improved team processes. The ACNP role components that were enacted were sensitive to the surrounding context and responsive to the needs of team members and patients. The patterns of communication among team members were aligned with the structural dimensions surrounding the team. A conceptual framework was developed. It highlighted the key inter-related dimensions and concepts that different stakeholder
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".