Host Agency Perspectives on Facilitating Community-Based Clinical Experiences for Nursing Students
Bibliographic record
Abstract
Nurse educators have developed many innovations for nursing education in response to the current shortage of clinical placements. Nursing programs are increasingly relying on placements in community-based agencies (some of which are not health agencies) and university-agency collaborations are being developed to enhance capacity for student placements. Our interest in this qualitative study was to elicit the perspectives of host agencies in these alternate sites regarding hosting undergraduate student clinical placements. Aims were to gain insight about 1) how community-based host agencies decide to host students, including how the number of students hosted is determined; 2) the nature of agency–university partnerships; and 3) the agency’s experiences of hosting students in relation to benefits and issues that accompany hosting students. Administrators from 25 community-based agencies that hosted undergraduate nursing students from the researchers’ university were invited to participate in an online survey or telephone interview, with a final sample size of 18 respondents. Each participant was given the option of a telephone interview but most elected to complete the online survey because of its flexibility; five participants were interviewed on the phone and 13 completed the open-ended survey questions online. Findings revealed that host agency decisions to host students could have an ad hoc nature to them, and were described as contingent on various explicit and implicit factors such as staff availability, the perceived contribution of students, or a philosophic commitment to contribute to student education. Smaller agencies reported and preferred informal partnerships with the educational institutions because they were perceived to be more flexible, and all sites emphasized the need for increased, improved communication with clinical instructors and more direct supervision of student placements by the educational unit. The benefits of hosting student nurses were described as outweighing the issues and challenges. The study also revealed incongruities between the perspectives of these host agencies and typically-held views of nursing programs, whereby host agencies spoke of the “ideal student” and nursing programs tend to seek an “ideal placement.” Further research is needed to generate knowledge about expanding capacity for clinical placements, enhancing partnerships, and enriching student learning outcomes.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".