Core SSW Services Working Paper: Referral, Screening, Assessment, & Service Delivery Process (RSASD) for School Social Workers
Bibliographic record
Abstract
The framework offered here is rooted in a variety of sources, primary among them my own research on SSW practice, consultation with the PLC Project Advisory Board, and the ongoing work with the various PLCs here in Ontario. I am grateful for all the different PLC members, co-leads, and outside research and practice experts that helped form this draft framework. Ontario SSW Managers and the 70+ Professional Learning Community (PLC) members identified significant concerns with how SSW struggled to manage the different expectations of their “host setting.” A primary concern was the lack of control many SSW reported having with how their caseload came about, specifically in how referrals were made for SSW services. This Plain Language Summary (PLS) will detail the key components of the proposed new Referral, Screening, Assessment, and Service Delivery process (RSASD). Drawing on the work of Dr. Kelly the leadership of the Assessment/Criteria PLC, we will identify the best evidence-informed practices (EIP) in screening and assessing student clinical concerns, and suggest some best practices that formed the basis for our new RSASD model. The EIP in each PLS will also be aligned where applicable with the evidence-based practice (EBP) Common Elements identified by School Mental Health-Assist (SMH-ASSIST). Additional recommendations for further reading and implementation strategies are included in the PLS, while a more extensive annotated bibliography and related materials are included elsewhere as part of all of the Ontario SSW PLC ToolKits.
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 | 0.048 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".