Feature Profile - Field Instructor Leyla Didari
Bibliographic record
Abstract
Leyla Didari is a Registered Social Worker (RSW) with two Master of Social Work (MSW) degrees from Iran and Canada. She currently works as a social worker at Compass ACT Team at Michael Garron Hospital with adults with chronic mental health issues. She is using an eclectic approach tailored to the unique needs of each client. Leyla has over 16 years of experience in a variety of settings and with diverse clients, including both patients with physical and/or mental health issues. Leyla is always challenging herself. She went through so many changes in her life, such as changing her career from nursing to social work, immigrating to Canada and starting over, learning a new language and going through various academic programs in her new country, such as IESW (International Educated Social Worker) Program at Ryerson University, as well as MSW Program at the University of Windsor, while working full time. She is continuously seeking to gain more skills and broaden her education. For instance, Leyla has spent many hours of training to become a WRAP (Wellness Recovery Action Plan) facilitator and a certified Auricular Acupuncture Specialist. Leyla tries to provide an environment of compassion and support to help her clients and families overcome obstacles to move forward and thrive. \n \nThis is what Leyla had to say about her experience working as a social worker in Canada and supervising York University social work 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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.615 | 0.383 |
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".