Being Paid to Be a Student: Risks and Rewards – An Exploratory Mixed-Methods Study
Bibliographic record
Abstract
Background: Baccalaureate nursing students conclude their education with a preceptorship experience to integrate and consolidate knowledge, skills, and attributes in a clinical setting. Within the Canadian context, there is a lack of understanding about the experience of student compensation during the preceptor experience. Objective: To understand the perceptions of fourth-year nursing students and their instructors regarding the benefits and challenges of students participating in a compensated preceptorship initiative. Methods: This study used an exploratory mixed-methods design. Participants were fourth-year students in a bachelor of science in nursing preceptorship course and their assigned instructors at a university in Western Canada. This study used a survey design to collect student perceptions about the benefits and challenges of having their preceptorship experience in either a compensated or a traditional model. Survey design also gathered faculty perceptions of the benefits and challenges of teaching in these two models. Results: A total of 25 student participants completed the survey, 23 of whom were satisfied with their choice to participate or not participate in the compensated model. The benefits of choosing to participate were being compensated, being part of the system, and having a job after completing their studies. Challenges include missing the student role, poor communication, and pushbacks from staff and other students. The benefits of choosing not to participate were the opportunity to stay in a chosen placement and being under less pressure, while challenges included financial strain and not having a job lined up after completing their studies. Five of seven faculty indicated that the compensated preceptorship experience provided less opportunity for consultation on program concepts and learning opportunities. Conclusion: Student participants were overall satisfied with their choice to participate or not participate. Faculty members were cognizant of the financial strain on students but worried about the impact on learning in compensated environments. Given the mixed perceptions noted by faculty and students, there is a need for better understanding of how best to implement these initiatives.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".