Evaluation of two-time zones online training to transform older people's care
Bibliographic record
Abstract
This paper reports the evaluation by trainees of the innovative online education initiative known as the e-TRIGGER (e-TRaining In Gerontology and Geriatrics) program, which targets healthcare professionals working with older adults in Africa, the Middle East, and Europe (AFMEE course) and in Asia-Oceania (ASIO). The e-TRIGGER programs are implemented under the auspices of the International Association of Gerontology and Geriatrics (IAGG). The first year of teaching of the AFMEE program (May 2023 to April 2024) and the third year of the ASIO program (January to December 2024) were evaluated by the students using a satisfaction survey implemented at the end of the year of teaching. Almost all trainees reported that the course met their personal objectives. A significant majority reported applying acquired knowledge directly (AFMEE, 75 %; ASIO, 78 %) and indirectly (AFMEE, 30 %; ASIO, 42 %) in their daily work. Over half reported improved skills in caring for older adults (AFMEE 65 %, ASIO 52 %). Around one-fifth reported a job or career promotion after course completion (AFMEE 21 %, ASIO 17 %). The evaluation highlights the significant impact and success of the e-TRIGGER program for most alumni. Key challenges of this innovative teaching program include ensuring financial sustainability and addressing specific training needs related to long-term care, dementia management, and technology integration. Future perspectives include expanding the program to Latin America (IAGG e-TRIGGER LATAM) and developing complementary, specialized short courses on specific areas of geriatric medicine and gerontology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".