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REPORT SECTIONS:    Intro    Predict     Psychographics     Align     Recommendations     Ahead    

Ahead
New Hampshire’s
Prevent Phase

Our final phase, Prevent, kicks off with the implementation of prevention efforts followed by impact measurement.

Adjustments to efforts will be made following the PDSA cycle approach to ensure continued improvement with each iteration.

 

The aim of the PAP Prevent phase is to learn what combination of aligned prevention efforts will most positively impact each unique community. The continuous quality improvement approach allows for ongoing adjustments reflecting the changing needs of community members.

The University System of New Hampshire, as the lead evaluator, will work with the Community Implementation Teams and DHHS to conduct a program assessment evaluation, tracking the new prevention alignment of services and communication to determine if the identified prevention services bundle is effectively improving outcomes for children, families, and communities. Community Implementation Teams will document their strategies, efforts, and PDSA cycles as well as what resources were leveraged.

For New Hampshire, improving social determinants of health for the residents of Manchester, the Winnipesaukee Region, and Greater North Country are of primary focus.

PAP Analysis Results
Publicly Available

We are very pleased that the PAP Program Needs Assessment Maps showing areas in the highest need of services and resources can be found at the New Hampshire Health and Services Data Portal under Community Collaborations.

These maps will help support local communities to implement targeted programs and align interventions that are likely to have the greatest impact.

Our goal is to expand the PAP Program statewide for New Hampshire so every community will have PAP resources to aid in their prevention efforts and improve outcomes for children.

Interested in collaborating on this project? Please click here.

Thank You, DHHS, for Partnering With PAP to Improve Child Welfare in New Hampshire.

Special thanks to the DPHS and DCYF teams for their incredible work and willingness to partner with PAP on this project.


We would like to recognize Acxiom for including Predict-Align-Prevent as a part of its “Data for Good” program.

Predict-Align-Prevent wishes to acknowledge that these materials are made possible by a grant and support from Casey Family Programs. Thank you.

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References

  • Daley D., Bachmann M., Bachmann B.A., Pedigo C., Bui. M.T., & Coffman J. (2016). Risk terrain modeling predicts child maltreatment. Child Abuse Neglect. 62:29-38. doi:10.1016/j.chiabu.2016.09.014. https://www.sciencedirect.com/science/article/pii/S0145213416301922
  • Predict Align Prevent (2019). Richmond, Virginia Technical Report. https://b9157c41-5fbe-4e28-8784-ea36ffdbce2f.filesusr.com/ugd/fbb580_2f1dda2ff6b84f32856bc95d802d6629.pdf

Predict-Align-Prevent does not endorse any particular program, service, or organization referenced in this report.


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