Class Members Enrolled In HMSV B302 Will Prepare An Evaluati ✓ Solved

Class members enrolled in HMSV B302 will prepare an evaluati

Class members enrolled in HMSV B302 will prepare an evaluation plan based on the ML 2 Logic Model. This assignment should be completed individually. Use course materials, particularly the Toolkit. Format: minimum 6 pages, double-spaced, 12-pt Times New Roman or Cambria, 1-inch margins, APA citations. Required Plan Sections: Program Description; Purpose of evaluation and identification of evaluation type; Ethical considerations; Stakeholders; Evaluation Design: 3–5 key evaluation questions; Evaluation data (include qualitative and quantitative); Data collection methods (include primary and secondary data; include at least one survey with a minimum of 10 questions); Data analysis; Limitations of evaluation design; Management and Monitoring: plan implementation timeline; Process for monitoring implementation; Strategy for dissemination and use of findings.

Paper For Above Instructions

Evaluation Plan for an ML 2 Logic Model-Based Program

This evaluation plan follows the ML 2 Logic Model framework and responds directly to the required sections. It outlines purpose, design, methods, monitoring, and dissemination strategies and includes an illustrative survey and timeline.

Program Description

The ML 2 program is a community-based intervention designed to improve behavioral health outcomes among young adults (ages 18–30) by combining motivational learning modules (ML) with peer support and skills coaching. Core components include eight weekly group sessions, one-on-one coaching, and digital skill supports. Short-term outcomes target knowledge gain and increased motivation; intermediate outcomes include behavior change (reduced substance use, increased help-seeking); long-term outcomes aim for improved social functioning and sustained recovery. The logic model maps inputs (staff, curriculum, technology), activities (sessions, coaching), outputs (attendance, completed modules), and outcomes (behavioral and quality of life changes) (Rossi et al., 2019).

Purpose of Evaluation and Type

The primary purpose is both formative and summative: formative evaluation will improve program delivery during early implementation; summative evaluation will assess effectiveness at 12 months. A mixed-methods, utilization-focused evaluation approach is recommended to ensure findings are actionable for stakeholders (Patton, 2015; Creswell & Plano Clark, 2018).

Ethical Considerations

Ethical safeguards include informed consent, confidentiality, voluntary participation, and data security consistent with AEA guiding principles (AEA, 2011). Special attention will be given to protecting sensitive behavioral health data, obtaining IRB or equivalent review, and developing protocols for responding to participant distress or disclosures of harm (Bamberger et al., 2011).

Stakeholders

Key stakeholders include program participants, peer coaches, program staff, funders, referral agencies, and community partners. Stakeholder engagement strategies include an advisory panel with participant representation, quarterly stakeholder meetings, and regular feedback loops to ensure relevance and buy-in (CDC, 1999).

Evaluation Design

This mixed-methods design will use a quasi-experimental cohort with pre-post measurement and comparison to a matched nonparticipant cohort where feasible. Process and outcome measures will be integrated to explain how implementation relates to outcomes (Rossi et al., 2019).

Key Evaluation Questions
  1. To what extent does ML 2 improve participants’ knowledge and motivation at program completion? (short-term)
  2. Does participation in ML 2 reduce substance-related risk behaviors over 6–12 months? (intermediate)
  3. How do implementation fidelity and participant engagement influence outcomes? (implementation-effect linkage)
  4. What participant-identified strengths and challenges affect program uptake and sustained behavior change? (qualitative)
Evaluation Data (Qualitative and Quantitative)

Quantitative data: standardized pre/post surveys (knowledge, motivation scales), behavioral measures (self-reported substance use frequency), attendance and module completion rates, and administrative data (referrals, service utilization). Qualitative data: semi-structured interviews with participants and staff, focus groups, coach session notes, and open-ended survey responses (Patton, 2015; Krueger & Casey, 2015).

Data Collection Methods

Primary data: baseline and follow-up surveys at 0, 3, 6, and 12 months; a program-specific 10-question minimum survey (see sample below); semi-structured interviews and focus groups with purposive sampling; fidelity checklists completed by supervisors. Secondary data: administrative and referral records, regional health statistics, and existing program monitoring databases. Mixed methods integration will triangulate findings (Creswell & Plano Clark, 2018).

Sample 10-Question Participant Survey (Core Items)
  1. How many ML 2 sessions did you attend? (numeric)
  2. Rate your confidence in managing stress (1–5 Likert).
  3. Rate your motivation to change substance use behaviors (1–5 Likert).
  4. In the past 30 days, how many days did you use alcohol? (numeric)
  5. In the past 30 days, how many days did you use cannabis or other substances? (numeric)
  6. How useful were peer support sessions? (1–5 Likert)
  7. Did you access one-on-one coaching? (Yes/No)
  8. How satisfied are you with the digital materials? (1–5 Likert)
  9. What barriers affected your attendance? (open-ended)
  10. Would you recommend ML 2 to a friend? (Yes/No and why) (open-ended)

Data Analysis

Quantitative analysis: descriptive statistics, paired t-tests or nonparametric equivalents for pre-post changes, regression models controlling for covariates to estimate program effects, and subgroup analyses for equity assessment (Bryman, 2016). Propensity score matching may be used to strengthen the comparison cohort. Qualitative analysis: thematic coding using NVivo or similar tools, framework analysis mapped to the logic model to identify mechanisms and contextual factors (Patton, 2015). Mixed methods integration will use joint displays to connect quantitative outcomes with qualitative process explanations (Creswell & Plano Clark, 2018).

Limitations of Evaluation Design

Limitations include nonrandomized design risks (selection bias), reliance on self-report behavioral measures, potential attrition bias, and resource constraints limiting sample size or longitudinal follow-up. Mitigation strategies include robust matching, triangulation with administrative data, validated measurement instruments, and proactive retention strategies (Bamberger et al., 2011).

Management and Monitoring

Plan Implementation Timeline (Summary)
  • Month 0–1: Finalize protocol, IRB approval, stakeholder advisory formation.
  • Month 2: Staff training, finalize survey instruments, pilot testing.
  • Month 3–8: Participant recruitment and program delivery (cohort 1), ongoing data collection.
  • Month 9–12: Follow-up data collection, fidelity assessments, interim analysis.
  • Month 13–16: Additional cohorts or replication, final analysis.
  • Month 17–18: Reporting, dissemination, and utilization planning.
Process for Monitoring Implementation

Monitoring will use routine performance dashboards (attendance, module completion, survey response rates), monthly implementation meetings, fidelity audits using standardized checklists, and quarterly stakeholder reviews to identify corrective actions. A data manager will oversee quality checks and missing data protocols (WHO, 2010).

Strategy for Dissemination and Use of Findings

Findings will be shared via tailored briefs for funders, policy briefs for community partners, a participant-friendly summary, and academic dissemination (conference presentations, peer-reviewed articles). An executive dashboard and a dissemination workshop with stakeholders will translate results into program improvements and scale-up decisions. Emphasis will be placed on actionable recommendations and feedback loops for continuous quality improvement (CDC, 1999; AEA, 2011).

Overall, this mixed-methods, utilization-focused evaluation aligns with the ML 2 logic model and balances rigor with practicality to inform program improvement and assess effectiveness while safeguarding participant welfare and stakeholder needs (Rossi et al., 2019; Patton, 2015).

References

  • American Evaluation Association. (2011). AEA guiding principles for evaluators. American Evaluation Association.
  • Bamberger, M., Rugh, J., & Mabry, L. (2011). RealWorld evaluation: Working under budget, time, data, and political constraints (2nd ed.). SAGE.
  • Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.
  • Centers for Disease Control and Prevention. (1999). Framework for program evaluation in public health. MMWR, 48(RR11), 1–40.
  • Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
  • Krueger, R. A., & Casey, M. A. (2015). Focus groups: A practical guide for applied research (5th ed.). SAGE.
  • Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE Publications.
  • Rossi, P. H., Lipsey, M. W., & Henry, G. T. (2019). Evaluation: A systematic approach (8th ed.). SAGE Publications.
  • Scriven, M. (1991). Evaluation thesaurus (4th ed.). SAGE.
  • World Health Organization. (2010). Monitoring the building blocks of health systems: A handbook of indicators and their measurement strategies. WHO.

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