Exploring the Decision-Making Process Behind the Loss of a Clinical Placement: Second-Year Nursing Students in the Special Care Nursery
Bibliographic record
Abstract
The purpose of this study was to explore how a Special Care Nursery (SCN) in a southern Ontario hospital decided to stop taking second-year nursing students for clinical placement. A qualitative intrinsic case study approach was utilized to guide and analyze twelve participant interviews. Participants were recruited using both purposeful and snowball sampling. Sharan Merriam (1998) was utilized as a theorist for the methodology and framework of this case study. Additionally, Leah Curtin’s (2014) six-questions for ethical decision-making in nursing management were used to develop the semi-structured interview guide. An overarching theme of Conflicting Messages was found, with three subsequent themes of 1) Contributing Factors, 2) Level that Decisions Happen, and 3) Outcomes of Decision-Making. Findings of this study indicated that the decision to cease placements in the SCN was likely made due to a culmination of factors, but a defined cause and process for decision-making was not found. Factors that were identified by participants as being influential in the loss of this placement included clinical instructors not supporting students, high unit acuity, negative attitudes towards students, uncertainty with the student scope of practice, nurse burnout, and systems issues. There was uncertainty surrounding who was involved in making this decision, which was attributed by participants to a lack of communication and collegiality between frontline staff and those in management positions. This led to unilateral decision-making, and a lack of departmental cohesion. Additionally, preferential placement opportunities were found to be offered to medical learners over nursing students. Implications were identified as wide reaching, including unit recruitment concerns, lack of exposure to the specialty of neonatal nursing, and the inability of nurses to fulfill their professional obligations of knowledge sharing. Ultimately, it was identified that the use of Curtin’s (2014) decision-making model alone lacked a formal process to guide how decisions in nursing management should be made, although it raises context specific questions that aid in understanding an issue at hand. The development of a comprehensive model for decision-making in nursing leadership would be beneficial to provide structure for how important choices are made in healthcare and improve transparency in decision-making.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".