Personalized Service Delivery for Young People and Families: A Synthesis Review
Bibliographic record
Abstract
This synthesis project, funded through a MCYS Strategic Research Grant, seeks to provide a foundation for developing a multi-disciplinary and cross-sectoral research approach to exploring ‘personalized service’ within the context of residential treatment services for children and youth. This focus was to include the continuum of pre-referral supports and interventions to post-discharge community and family re-integration and sustainable outcomes. A detailed literature review about current approaches to ‘personalizing services’ in Ontario, Canada, North America, and the United Kingdom was undertaken with an emphasis on identifying the core conceptual and logistical principles that frame approaches to personalized services. The approach was intended to encourage future research that would focus on identifying and testing the evidence supporting or challenging such principles. The literature review was structured around six core themes: The stories and experiences of challenging and successful personalized approaches (satisfaction) as compared to outcomes (client change). Diversity considerations, including cultural identity, linguistic groups, and gender orientations. Service outcomes at the client level, at the agency level & at the system level. Successful tools and protocols for implementing personalized services. Quality assurance protocols. Logistical and human resource considerations in service provision.
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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".