“I’ve found my voice”: Wraparound as a Promising Strength-based Team Process for High-risk Pregnant and Early Parenting Women
Bibliographic record
Abstract
The purpose of this project was to offer wraparound facilitation and process to high-risk pregnant and early parenting women and their infants/young children in the South Fraser region of British Columbia, Canada, as a means of improving their health and social well-being. Eighteen families were involved in the wraparound project, with seven participating in the evaluation. Significant lessons were learned on effective means of engaging “hard to reach” families. Process and outcome evaluation demonstrated that the project provided “high fidelity ” wraparound, which resulted in significant improvements in access to health care, birth outcomes, families ’ health and well-being, housing and nutritional status of women and their children, reduced risk from the use of substances, improved parenting outcomes, fewer removals of children, and an increasing move towards family reunification. Descriptive analysis indicates the importance of distinguishing wraparound from other team-based supports and avoiding labelling processes as wraparound that do not adhere to its philosophy and practices as measured by the wraparound fidelity index.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".