All applications are reviewed by a panel of Bierman clinicians and organizational leaders. To ensure fairness, all papers are de-identified before review.
This scholarship is merit-based. There are no “right” answers. Reviewers are looking for authenticity, data-informed thinking, and genuine reflective growth.
How essays are evaluated
Each essay is scored across four weighted criteria for a total of 100 points. Each criterion is scored independently on a 0–25 scale using six levels: Not Present (0), Minimal (5), Developing (10), Proficient (15), Strong (20), and Exemplary (25).
Reviewers do not average across the descriptors. They award the single score that best matches the overall quality of that criterion, and record a brief rationale.
Scoring rubric
1) Supervision Context & Specificity of Data Use — 25 points
Prompt part 1: Describe a time when you or your supervisor used (or could have used) data to guide supervision practices or staff performance improvement.
What we’re looking for: a clearly described supervision or staff scenario, specific about what was measured and why, with the candidate’s role and the supervisory rationale clearly established, distinct from client-directed data.
High-scoring responses ground the scenario in a recognizable supervisory framework (Behavioral Skills Training, performance management, OBM, competency-based supervision), naming specific behaviors and data sources, with the supervisory rationale unmistakably clear throughout. Lower-scoring responses mention a supervision or staff scenario only in passing, reference “data” generically without specificity, or blur client-directed data with staff/supervisory data instead of distinguishing the two.
2) Data Type, Analysis & Behavioral Outcomes — 25 points
Prompt part 2: Explain the type of data used, how it was analyzed, and what behavioral changes resulted for staff or the supervisory relationship.
What we’re looking for: a specific data type and analysis method (for example, IOA, permanent products, performance scorecards, or direct observation checklists), with a clear, evidence-based connection between what the data showed and the behavioral changes that followed for staff or the team.
High-scoring responses show a sophisticated understanding of how behavioral data functions differently in supervision versus direct client work, referencing specific measurement approaches (inter-observer agreement, direct observation coding, skills-based assessments, permanent product review) and articulating the data-to-behavior-change chain with precision. Lower-scoring responses reference data without naming a specific type or analysis method, or describe change in vague terms (“things got better”) with little connection back to the data.
3) Reflective Growth & Evolving Definition of “Progress” — 25 points
Prompt part 3: Reflect on how applying data to supervision changed your view of what “progress” means, for both clients and teams.
What we’re looking for: a genuine reflection on how the experience reshaped the candidate’s view of progress, addressing both the client and the team or staff dimension, and explaining what’s distinct about measuring progress in a supervisory context.
High-scoring responses integrate the scenario, the data, and the reflection into a unified, systems-level insight that spans clients, staff, and organizational outcomes, with the reflection feeling earned and personal rather than academic. Lower-scoring responses offer vague or formulaic statements about the experience (“I realized data is important”) or address only the client or the team dimension without connecting the two.
4) Writing Quality, Authenticity & Depth of Reflection — 25 points
Evaluates clarity, organization, authentic voice, and genuine engagement with the prompt across the full essay.
What we’re looking for: clear, well-organized writing with an authentic personal voice, appropriate length (1,000–1,500 words), and reflection that goes beyond the obvious.
High-scoring responses are compelling and memorable, with a distinctive voice and sophisticated reflection. The reader can clearly picture who this candidate is and why they belong in this field. Lower-scoring responses are generic, formulaic, or impersonal, fall significantly outside the word range, or show little evidence of genuine engagement with the prompt.
Note: Per the AI use policy, essays suspected of being AI-generated are not scored and are flagged for disqualification.
Score summary
Criterion — Max Points
Supervision Context & Specificity of Data Use — 25
Data Type, Analysis & Behavioral Outcomes — 25
Reflective Growth & Evolving Definition of “Progress” — 25
Writing Quality, Authenticity & Depth of Reflection — 25
Total Score — 100
Important notes for applicants
- De-identified review: judges do not see your name when scoring.
- Original work: your submission must be your own. Cite collaborators or sources as appropriate (see Terms & Conditions).
- Clarity over jargon: write plainly. Accuracy and thoughtfulness matter more than advanced terminology.
- Stay within the prompt: Round 3 centers on “Data-Informed Supervision.”
Questions?