Goal Setting in Speech–Language Pathology: A Pilot Test of a ‘One‐Size‐Fits‐All’ Planning Framework
Bibliographic record
Abstract
BACKGROUND: Developing treatment goals and hierarchies is fundamental to effective intervention. Despite this, interventions are often vaguely or ambiguously described, negatively impacting outcome measurement, client engagement, and team communication. THIMS (Target, Hierarchy, Ingredients, Measures, Success Criterion) is a novel intervention planning framework that aims to improve the specificity and measurability of treatment goals and hierarchies in speech-language pathology (SLP). AIM: The objective of this pilot study was to determine if clinician training in the use of the THIMS Framework was feasible and effective at improving the specificity and measurability of SLP treatment goals and hierarchies. METHODS: We completed a within-group pre-post pilot study to determine the impact of training speech-language pathologists (SLPs) to use the THIMS Framework. We evaluated participant recruitment, task completion, and attrition, as well as the specificity and measurability of SLP goals and hierarchies submitted before and after training. RESULTS: Twenty-three SLPs completed the study, with participants representing a broad range of years of experience and clinical practice areas. Results showed feasible recruitment and retention and significantly higher scores for treatment goals and hierarchies after completing THIMS training sessions. CONCLUSION: Training in the use of the THIMS Framework was feasible for a small sample size and resulted in increased specificity and measurability of SLP treatment goals and hierarchies. This 'one-size-fits-all' framework has the potential to fill the current gap for a systematic but flexible goal and hierarchy writing system for SLPs. WHAT THIS PAPER ADDS: What is already known on this subject For many years, the importance of specificity in goal writing and intervention planning has been highlighted as critical for ensuring client engagement, objective outcome measurement, and effective team communication. Despite this, studies continue to reveal a disconnect between clinicians acknowledging the importance of precision in goal formulation and actually applying the required level of specificity in their own treatment plans. What this paper adds to existing knowledge This pilot study describes the evaluation of a novel intervention planning framework that was designed to ensure specificity and measurability of goals and treatment hierarchies. The framework draws on elements of SMART goals and the Rehabilitation Treatment Specification System to provide clinicians with a stepwise process to plan interventions for a wide range of goal areas and populations. What are the potential or actual clinical implications of this work? To our knowledge, THIMS (now TACSI) is the only framework that provides explicit guidance on how to write specific and measurable SLP treatment goals and hierarchies that apply to any client population in any practice setting. The present study provides initial evidence that this framework has the potential to become a 'one-size-fits-all' SLP intervention planning system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".