Needs-Based Mentoring in Schools: A Holistic Approach for Working with Youth at-Risk
Bibliographic record
Abstract
This article describes a theoretical framework for a school-based mentoring (SBM) approach that addresses identified limitations of current adult-youth mentoring practices in schools and augments them with evidence-based components. The Needs-Based Mentoring (NBM) approach includes five elements: (1) Engage, (2) Teach, (3) Structure, (4) Advocate, and (5) Assess. These elements include component practices. Throughout NBM, mentors collaborate with mentees to identify goals based on an informal assessment of the student-mentee’s willingness and ability to engage in the activities involved in each element of mentoring. A description of each element is provided, as well as recommendations for training school-based professionals (e.g., teachers, administrators, school counselors, staff and paraprofessionals) as mentors for efficiency and sustainability. This applied approach will enhance school-based mentoring for the future of one-on-one targeted mentoring with youth at-risk of school dropout to improve school engagement and social-emotional outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".