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Opportunity & Workforce

Pipeline 09D

Pathways that pull youth back into school and work: mentoring and credible-messenger models, re-engagement centers, CTE programs, work-based learning, and apprenticeships that lead to real paychecks and staying power.

What We Track

Re-engagement (Education)

  • Days to re-enrollment after release or disposition (target ≀10)
  • Route used: school, GED/HiSET, credit recovery, alternative-ed bridge, virtual

Career & Technical Education (CTE)

  • Program enrollment by pathway (e.g., construction, health, IT)
  • Industry-recognized credentials attempted/earned per youth

Work-Based Learning (WBL)

  • Internships, paid placements, summer youth employment participation
  • Supports provided: transportation, tools/PPE, stipends

Apprenticeships

  • Pre-apprenticeship completions and transitions to Registered Apprenticeship
  • Placement rate from pre-apprentice β†’ apprentice within 90 days

Mentoring / Coaching

  • Credible-messenger or coach assigned (name/contact; start date)
  • Engagement: meeting cadence met; persistence through 30/90/180 days

Typical Flow

  1. Assessment & plan β€” education status, career interests, barriers (transport, childcare)
    • Set target route (school, GED/HiSET, CTE); assign mentor/coach
  2. Re-engage β€” enroll in school or GED/HiSET; activate supports
    • Confirm schedule; arrange transport/tools; begin tutoring as needed
  3. Skill-build β€” CTE coursework + WBL placement
    • Track hours, competencies, and onsite evaluations
  4. Credential & place β€” earn industry certs; place into first job or apprenticeship
    • Document offer date, start date, wage/hours (where available)
  5. Stabilize β€” follow-ups at 30/90/180 days
    • Attendance, credits/credentials, employment retention; adjust supports
Schema source: opportunity-and-workforce

Fields

FieldTypeRequiredCodesetDescription
pipeline_place_iduuidβœ…β€”Unique identifier for this opportunity/workforce episode.
pipeline_stage_idenumβœ…
stages.yml#stage_key(8)
  • intake
  • detention
  • adjudication
  • disposition
  • supervision
  • commitment_and_placement
  • reentry
  • closure
One of the canonical stages; here = reentry_aftercare.
pipeline_place_keyenumβœ…
pipeline_places.yml#place_key(45)
  • intake
  • pre_petition_diversion_and_deflection
  • diversion
  • youth_assessment_tools
  • prosecutorial_screening
  • status_offenses
  • adjudication
  • case_planning
  • family_engagement
  • case_timeliness
  • language_access
  • restorative_justice
  • detention_screening
  • community_atds
  • electronic_monitoring
  • court_appearance_and_fta
  • initial_hearing
  • pretrial_supervision
  • probation
  • intensive_supervision
  • …and 25 more
Canonical key for this place (maps to route/slug).
youth_idstringβœ…β€”Salted/hashed or state UID. No plaintext PII.
staff_idstringβ€”Reentry/aftercare staff coordinating education/work supports.
occurred_datetimedatetimeβœ…β€”Anchor timestamp (e.g., route selection or first program enrollment).
jurisdiction_codestringβœ…β€”County/parish/circuit or standardized local code.
source_systemstringβœ…β€”Origin system name.
source_filestringβ€”Source batch/file id if flatfile.
extract_run_idstringβ€”ETL run id for lineage.
series_break_flagbooleanβ€”Comparability break applies to this row.
series_break_reasonenum
series_breaks.yml#reason(4)
  • definition_change
  • vendor_change
  • coverage_change
  • policy_change
Reason for break (policy/process/tool change).
release_or_disposition_datedateβ€”Anchor to measure re-enrollment timeliness.
reengagement_route_codeenum
reengagement_routes.yml#route(5)
  • school
  • ged_hiset
  • credit_recovery
  • alt_ed_bridge
  • virtual
school, ged_hiset, credit_recovery, alt_ed_bridge, virtual.
re_enrollment_start_datedateβ€”First active enrollment date per selected route.
elapsed_days_to_reenrollmentintegerβ€”Structural store of days from release/disposition to re_enrollment_start_date.
school_provider_idstringβ€”Receiving school/LEA (if route = school or alt_ed_bridge).
cte_provider_idstringβ€”Training/CTE provider id.
cte_pathway_codeenum
cte_pathways.yml#pathway(11)
  • construction
  • health_sciences
  • information_technology
  • manufacturing
  • automotive
  • culinary
  • hospitality
  • business
  • agriculture
  • public_safety
  • other
CTE pathway (e.g., construction, health_sciences, it, culinary).
cte_enrollment_datedateβ€”Date of CTE program enrollment.
credential_type_codeenum
credential_types.yml#type(6)
  • hs_diploma
  • ged
  • industry_cert
  • short_term_training
  • associate_degree
  • other
Industry-recognized credential type attempted/earned.
credential_attempted_datedateβ€”Date of first attempt at credential.
credential_earned_flagbooleanβ€”True if credential earned during this episode.
credential_earned_datedateβ€”Date credential earned (if earned_flag true).
wbl_type_codeenum
wbl_types.yml#type(8)
  • internship
  • paid_placement
  • summer_youth_employment
  • job_shadow
  • pre_apprenticeship_project
  • service_learning
  • cooperative_education
  • other
internship, paid_placement, summer_youth_employment, job_shadow, pre_apprenticeship_project.
wbl_host_provider_idstringβ€”Host site/employer for WBL.
wbl_start_datedateβ€”WBL start date.
wbl_end_datedateβ€”WBL end date (if concluded).
wbl_hours_completedintegerβ€”Total hours completed in WBL.
wbl_evaluation_outcome_codeenum
evaluation_outcomes.yml#outcome(4)
  • satisfactory
  • needs_improvement
  • unsatisfactory
  • not_evaluated
satisfactory, needs_improvement, unsatisfactory, not_evaluated.
supports_provided_codesarray<string>
support_services.yml#type(7)
  • transportation
  • mentoring
  • employment
  • tutoring
  • counseling
  • family_support
  • other
Supports used (transportation, tools_ppe, stipend, tutoring, mentoring); semicolon-delimited.
pre_apprenticeship_completion_flagbooleanβ€”True if pre-apprenticeship requirements completed.
pre_apprenticeship_completion_datedateβ€”Date of pre-apprenticeship completion.
apprenticeship_transition_status_codeenum
apprenticeship_transition_statuses.yml#status(4)
  • transitioned_within_90d
  • transitioned_after_90d
  • in_progress
  • not_transitioned
transitioned_within_90d, transitioned_after_90d, not_transitioned, in_progress.
apprenticeship_start_datedateβ€”Registered Apprenticeship start date (if transitioned).
employer_provider_idstringβ€”Employer/provider for apprenticeship or job placement.
job_offer_datedateβ€”Date of first job offer in this episode.
job_start_datedateβ€”First day worked in placed job.
scheduled_hours_per_weekintegerβ€”Scheduled weekly hours at start.
hourly_wage_amountnumberβ€”Hourly wage at start (currency not stored; handle upstream).
employment_status_codeenum
employment_statuses.yml#status(5)
  • placed
  • pending
  • searching
  • not_placed
  • declined_offer
placed, pending, searching, not_placed, declined_offer.
mentoring_provider_idstringβ€”Mentoring provider assigned.
coach_staff_idstringβ€”Coach/credible messenger staff id.
mentoring_start_datedateβ€”Date mentoring/coaching began.
meeting_cadence_interval_codeenum
review_intervals.yml#interval(3)
  • review_7d
  • review_14d
  • review_30d
Planned cadence for meetings (e.g., weekly, biweekly, monthly).
engagement_status_30d_codeenum
engagement_statuses.yml#status(4)
  • active
  • sporadic
  • disengaged
  • unknown
Engagement snapshot at 30 days (active, sporadic, disengaged).
engagement_status_90d_codeenum
engagement_statuses.yml#status(4)
  • active
  • sporadic
  • disengaged
  • unknown
Engagement snapshot at 90 days.
engagement_status_180d_codeenum
engagement_statuses.yml#status(4)
  • active
  • sporadic
  • disengaged
  • unknown
Engagement snapshot at 180 days.
Download CSVwhat_we_track.csv

Data & Methods

Education re-engagement uses an enrolled-student denominator; employment outcomes use placed-youth cohorts with follow-up at 30/90/180 days. Track credential counts and acceptance via district crosswalks. Apply small-n pooling over 2–3 years with suppression flags. Equity lenses (race/ethnicity, disability, rural/urban) are applied to access and outcomes. When program definitions change (e.g., WBL category updates), mark series breaks.

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Transparency note: Employment and credential data often lag; we mark data vintage on charts and pool small-n cohorts to protect privacy while keeping trends interpretable.