My placement at the Complex Developmental Behavioural Conditions Department at Sunnyhill Health Centre
Bibliographic record
Abstract
My practicum at the Sunnyhill Complex Developmental and Behavioural Conditions Department has taught me about the role of Social Work within an interdisciplinary team in a medical setting, and the value of aligning with the needs and strengths of families and caregivers. This practicum has also informed me on the function of a diagnosis in determining community supports. My preparatory research included youth with developmental disabilities, the assessment process, the need for more significant structural support, the life-needs model, the portage model, how to support families and caregivers, social work within an interdisciplinary team, mindfulness and outcome evaluations. My learning experience has implications for my future practice working within interdisciplinary teams. In my future work, I aspire to promote social justice and ethical practice by using my skills and expertise of external and community resources to overcome barriers in culturally sensitivity ways that honours the client’s unique strengths, needs, and their selfdetermination. My experience and learning enriched my professional capacity and my appreciation of the value of Social Work in supporting families and individuals.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.123 | 0.018 |
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".