Establish a Starting Approximation From Baseline

To establish a starting approximation from baseline, sample Maya's current response under relevant cues, ordinary supports, accessible response forms, and representative opportunities. Record the response distribution, prompts, reinforcement history, invalid events, context, and Maya's experience. Choose a first criterion that Maya can contact without manufactured failure. Preserve the complete baseline instead of selecting one best response as the starting level.

Sample the real cue and supports

Observe the label request, printer, saved profile, workspace, and ordinary help route Maya will use. Baseline without normal access answers a narrower and often less useful question.

Collect a response distribution

Record how far the response progresses on every valid opportunity. Include partial forms, latency, prompts, errors, withdrawal, and context. Preserve raw counts rather than reducing baseline to one percentage.

Keep invalid events visible

Retain disconnections, missing materials, inaccessible controls, interrupted opportunities, and absent cues in planned coverage. Repair the system before interpreting the response distribution.

Review reinforcement history

Ask what has followed each response form, whether partner help arrived early, and whether the completed label itself provides a useful natural outcome. History can affect what appears during baseline.

Choose a reachable first criterion

Use current evidence, practical value, response effort, access, and Maya's feedback. State a small verification sample, fallback, and review owner. A first criterion remains a revisable clinical decision.

Use this sequence to establish a starting approximation from baseline

Define Maya's meaningful outcome, sample representative valid opportunities, preserve the distribution and system failures, review history and experience, select a reachable first criterion, and verify it prospectively.

Build Maya's starting-approximation baseline record

Create one versioned starting-approximation baseline record for the shared-kitchen label-printing review. Include Maya's priority, terminal response, current response, changing dimension, unit, natural cue, eligible opportunity, ordinary supports, first and later criteria, response values, reinforcement due and delivered, prompts, latency, correction, access, health, exposure, advancement, hold, rollback, stop, authority, effective dates, integrity, agreement, invalidity, missingness, withdrawal, direct experience, burden, generalization, maintenance, result, uncertainty, decision, correction, and review trigger. Store only decision-relevant information with role-limited access.

Validate Maya's shaping evidence

Reproduce Maya's 18 planned, three invalid, and 4 + 7 + 4 = 15 valid opportunities. Audit cue, task version, step boundaries, saved profile, prompts, partner actions, consequence history, latency, invalidity, and Maya's report. Compare median, modal, minimum, and maximum performance without letting any one statistic choose the criterion. Verify the first approximation in a small, prospectively defined sample before it becomes a teaching rule.

Keep Maya's measures and experience separate

Measure opportunity coverage, response values, criterion attainment, reinforcement integrity, prompt exposure, access, generalization, and maintenance with their own units. Ask Maya separately about clarity, effort, comfort, usefulness, burden, and whether assessment should continue. Accurate implementation can coexist with poor fit. A valued outcome can also improve without evidence that shaping alone produced the change.

Work through Maya's example

Maya has 18 planned label-printing opportunities. Three are invalid because the printer disconnects. Across 15 valid opportunities, four include one completed setup step, seven include two, and four include three. No opportunity includes all four terminal steps. The distribution is 4 + 7 + 4 = 15. A starting criterion can use current, repeatable performance after access is stable. Preserve every planned and valid unit, raw count, denominator, criterion version, response value, reinforcement event, prompt, latency, access state, integrity result, withdrawal, direct report, system failure, correction, and unresolved item. This fictional example demonstrates one assessment control. It offers no universal shaping sequence, step size, reinforcement rule, independence, mastery, generalization, diagnosis, trait, treatment effect, legal conclusion, coverage decision, payment promise, or outcome guarantee.

Address Maya's main assessment risk

Choosing three steps from Maya's best four trials ignores the seven two-step responses and may create repeated failure. Choosing one step from the minimum ignores useful current performance. A representative starting point needs the distribution, opportunity quality, effort, and Maya's view. Review goal relevance, response and dimension definitions, opportunity quality, ordinary supports, access, health, effort, prompts, reinforcement, partner behavior, context, client experience, authority, and causal scope separately. A clean graph cannot repair an inaccessible, burdensome, unwanted, or ethically unsound arrangement.

Keep shaping concepts and authority clear for Maya

The ABAI Basic Principles page supplies broad higher-education context. The BACB Test Content Outline includes shaping, differential reinforcement, measurement, prompting, generalization, maintenance, and evaluation as examination content. The current BACB Ethics Code addresses competence, client involvement, consent and assent when applicable, medical needs, positive reinforcement, risk, data, documentation, and evaluation for covered people. These sources create no individualized shaping plan or practice authority for Maya.

Distinguish gradual-change procedures for Maya

Vollmer's gradual-change review distinguishes shaping changes to response requirements from fading changes to stimuli, chaining changes to task components, and schedule thinning changes to reinforcement schedules. It is a conceptual review. Use the distinction to describe what changes in Maya's plan, while the qualified clinician selects and evaluates an individual procedure.

Read the small shaping comparison cautiously for Maya

Shane and colleagues compared a specified vocal-shaping condition with a broad reasonable-attempt condition for three autistic preschoolers and reported faster mastery under shaping in that study. The small, older, narrow comparison supplies no universal response form, step size, progression rule, or expected result for Maya.

Keep criteria and follow-up visible for Maya

For Maya's starting-approximation baseline record, McDougale and colleagues provide a reporting caution from 157 skill-acquisition articles in selected behavior-analytic journals. In Maya's review, remember that 26% lacked a specific mastery-criterion method, 58% lacked maintenance probes, and 1% were unclear about maintenance. This descriptive evidence supports transparent criteria and follow-up; it identifies no single best shaping threshold.

Preserve treatment scope, AAC, assent, and access for Maya

The CASP public summary supplies high-level ABA behavioral-health-treatment scope for autistic people, with licensed detail outside this page. ASHA's AAC portal says AAC users should always have access to their communication tools or devices. Breaux and Smith provide assent-focused practice guidance in an evolving evidence base. Follow governing sources and preserve communication, health care, food, water, bathroom use, mobility, rest, relationships, safety, and emergency help for Maya.

Choose Maya's next bounded action

Maya selects a first criterion of two setup steps with the saved printer profile available and asks the team to treat connectivity failures as system events. Record the qualified owner, supporting evidence, effective date, current criterion, ordinary supports, health and access state, implementation check, accessible explanation, disagreement route, and reassessment trigger. Preserve the original assessment when responses, criteria, people, systems, tasks, health, or priorities change. A new arrangement creates a dated version rather than an error in Maya's earlier performance.

Close Maya's shaping review

Review the starting-approximation baseline record with Maya, the qualified behavior analyst, relevant partners, access owners, and specialists named in the manifest. Confirm that each criterion and denominator is reproducible, response shaping stays separate from prompting, fading, chaining, and schedule changes, AAC and basic access remain protected, health and motor questions stay with qualified professionals, and conclusions remain bounded to the sampled response, task, people, and context. Keep this page draft and noindex until every required review is complete.

How to Establish a Starting Approximation From Baseline