An in-depth examination of geriatric nursing master's programs: A comparative analysis between Iran and Canada
Bibliographic record
Abstract
This study was undertaken with the explicit objective of comparing the educational curriculum of the Master's program in Geriatrics in Iran with that of the Alberta Nursing School in Canada. This comparative descriptive study employed George F. Bradley's 4-step method, involving the stages of description and proximity analysis. Data were sourced from internal databases, including Magiran and Scientific Information Database, supplemented by external databases such as Google Scholar, PubMed, and Scopus. The geriatric nursing master's program in Alberta is characterized by a longer duration and a more established history. The delineation of roles for graduates in this context is more specific compared to the situation in Iran. The admission criteria in Alberta are more stringent, emphasizing the quality of candidates through a comprehensive evaluation of general and professional factors. Conversely, the admission process for nursing master's students in Iran considers a broader set of criteria. Furthermore, a substantial disparity exists between the course topics in Iran and the pressing societal and clinical needs. The inadequacy of clinical space and the scarcity of experienced professors emerge as significant challenges within the training program in Iran. The formulation of the curriculum for the geriatric nursing master's program should adhere to a systematic and comprehensive framework that aligns with societal needs. Graduates of the program should possess a well-defined professional position within the healthcare landscape. The educational process should involve the utilization of accomplished professors and a dynamic clinical environment to effectively train 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".