Impact of a Parent Education Program Delivered by Nurses and Health Care Providers in Reducing Infant Physical Abuse Hospitalization Rates in British Columbia, Canada
Bibliographic record
Abstract
Background The Period of PURPLE Crying Program® ( PURPLE ) is a universal parent education program that is delivered by nurses and health care providers to all parents/caregivers of newborns in British Columbia (B.C.). The aim of the program is to reduce the incidence of Traumatic Head Injury -Child Maltreatment (THI-CM), a form of child physical abuse. Objective To determine if the PURPLE program had an impact on the rate of physical abuse hospitalizations for children less than or equal to 24 months of age in B.C. since implementation in 2009. Methods The analysis measured physical abuse hospitalization rates for the period January 1, 1999 to December 31, 2019 and excluded any cases of confirmed Traumatic Head Injury-Child Maltreatment. Data were divided into pre-implementation period January, 1999 to December, 2008, and post-implementation period January, 2009 to December, 2019. Data were obtained from the Discharge Abstract Database and B.C. THI-CM Surveillance System to capture information on infant child abuse. Poisson regression and ANCOVA was applied to model the change in rates pre and post program implementation. Results Physical abuse hospitalization rates decreased by 30% post-implementation period (95% CI: −14%, 57%, p = 0.1561). The decreasing linear trend in the post-implementation period was significantly different than the increasing linear trend in the pre-implementation period (F 1,17 = 4.832, p = 0.042). Conclusions Nurses’ role in engaging parents in conversations about PURPLE messages over multiple timepoints within a structured universal program model resulted in a decrease in physical abuse hospitalization rates since the implementation of PURPLE .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".