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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".