Preceptorship in nursing education in Ontario: Rhetoric vs. reality
Bibliographic record
Abstract
The purpose of this quantitative and qualitative study was to provide a detailed view of how preceptorships were organized and experienced in university and college settings in Ontario in 1997–98. One important difference between university and college preceptorships was that diploma programmes tended to have more of a work focus and university programmes a learning focus. While there was clearly overlap in these distinctions, these contrasts in rationale were apparent in the handbooks from these programmes. When this study was done in 1997–98, health care agencies were feeling the effects of restructuring in the health care system and universities and colleges were faced with budgetary restraints. My study showed that this impacted negatively on those closely involved with preceptorship experiences. Managers were left to select preceptors, using their own criteria. I found that the learning environment in these stressed workplaces was less than ideal for students and at times they felt exploited. Students missed out on opportunities for critical thinking because their preceptors were rarely provided time to teach and help them reflect on patient care. In spite of workload issues and lack of recognition, preceptors were willing to take on the responsibility of being a preceptor. Intrinsic rewards were more important to them than being paid for this work. While faculty were less satisfied with preceptored experiences and their ability to support preceptors, preceptors in my study seemed content with the amount of support they received. Support from colleagues and managers in the practice setting was more important to them. While the nursing education system in Ontario has changed since 1998, preceptored experiences have not appreciably changed. There is controversy about the frequency and focus of these experiences, but I would argue that the emphasis should always be on student learning and having faculty pay more attention to preceptors and how they teach. Funding for nursing clinical education must be made more equitable than in the past so that resources are there to offset the increased time and effort that is needed to address the disjunction between the rhetoric of teaching and the reality of the practice settings. The tendency in the nursing literature to lay the blame for these problems with faculty or preceptors fails to recognize that cutbacks and restructuring have exacerbated them.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".