Producing Flexible Nurses: How Institutional Texts Organize Nurses’ Experiences of Learning to Work on Redesigned Nursing Teams
Bibliographic record
Abstract
The purpose of this qualitative research was to utilize an institutional ethnographic (IE) lens to trace how various institutional (regulatory, educational, union, governmental, or health authority) texts and resources organize baccalaureate (RN) and diploma (vocational or practical) nurses’ experiences of learning to practice on acute care teams. Functional care models have been introduced in acute care, creating RN-LPN-health care aide (HCA) teams in conjunction with expanded practice scopes for LPNs. Questions arise as to how nurses (RNs and LPNs) learn to work together on these intra-professional teams. Beginning from the standpoint of front-line workers provides an entry-point into understanding how institutional priorities organize the everyday work of people. Ten RNs and ten LPNs were interviewed in two small community acute care hospitals on Vancouver Island. More specifically, in observations and interviews we looked for ways in which textually mediated work processes (such as regulatory, governmental, health authority, and educational documents) and other conceptual resources influenced nurses’ understandings of nursing education and professional practice. This analysis focused on how RNs and LPNs learn to work on re-designed nursing teams and traced the textually mediated discourses that are organizing this learning in the context of recent changes to LPN education and nursing teams. Our findings highlight unarticulated nursing knowledge/thinking, and the textual insertion of functional, skilled and flexible worker discourses, which organize to blur practice between RN and LPNs making them (potentially) interchangeable in complex acute care contexts. This study, situated as one analysis among a larger study, shows the invisibility of nursing disciplinary and professional goals and knowledge in nurses’ talk, as RNs and LPNs re-learn and sustain nursing practice in ways that fulfill other institutional and organizational goals. This realignment has significant implications for educators in nursing programs, who participate in teaching within educational silos. This research has shown that the absence of clarity in functional roles (perpetuating role confusion and ambiguity) is purposeful, with the goal of creating flexible workers.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".