Bibliographic record
Abstract
Context The university community encompasses diverse characteristics, including various age groups and cultures. Since 2010, the number of international students in Canada has doubled (Yamba et al., 2021). The university’s reality requires expertise in primary care, which is provided by registered nurses (RNs) and nurse practitioners (NPs) specializing in primary health care (PHNs). They contribute to proximity of care by promoting access to health care and services for the university community, including the student community, which is among the clientele that underuse health care system resources (Romo et al., 2019). Furthermore, international students generally do not have access to a family doctor. Thus, it was necessary to make health care and services accessible to our university community through a clinic. Objective, population and setting The objective is to develop and implement the CUSI, a nurse-led clinic, to increase health care access for the entire university community of Chicoutimi, Quebec, Canada. The clinic’s mission is threefold: clinical practice, teaching and research. Study The development of the clinic followed the ten steps to setting up a nurse-led clinic (Hatchett, 2008) and the AGILE approach to project development (Cobb, 2011; Project Management Institute, 2018). The AGILE approach facilitates the use of an iterative cycle process in collaboration with the community and partners. It was operated based on the organic nursing model proposed by Contandriopoulos et al. (2017), a hypothetical model which emphasizes intra-professional collaboration and nursing expertise. This model enables the full occupancy of the clinical nurse role through the support of PHNs. Analysis and outcomes measure We analyzed the consultations using quantitative descriptive statistics, including continuous variables (number of consultations and visits with RNs or PHNs, number of medical referrals, number of reserved activities) and categorical variables (number of persons by age group, access to a family doctor). Results The CUSI was opened in February 2023. Half of the university students who visited the CUSI do not have access to a family doctor and are international students. Only 10% of the visits could not be handled exclusively by RNs and PHNs. Conclusions The CUSI was implemented to provide care to university community employees and students.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".