Progress monitoring measures: A brief guide.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is much evidence to suggest that psychotherapy is effective, however, it is far from flawless (e.g., Lilienfield, 2007; Stuart, 1970). As the field of mental health changes, there has been a recent movement in routine practice toward the use of standardized measures to track client progress and to collect feedback about treatment response (Lambert & Shimokawa, 2011). The use of standardized tools can help practitioners identify when clients are not progressing in therapy and have been linked to better outcomes for nonresponsive clients than when these measures are not used (e.g., Shimokawa, Lambert, & Smart, 2010). The purpose of this article is to introduce a group of such tools, referred to as progress monitoring (PM) measures, and to highlight features relevant in selecting and implementing a PM measure in practice. Areas covered include domains assessed, target populations, administration, scoring, feedback and interpretation, cost, training and privacy. While there exist numerous outcome and assessment measures (e.g., Froyd, Lambert, & Froyd, 1996), this article focuses specifically on seven popular progress monitoring measures for adult mental health populations, that are brief, comprehensive and easily accessible tools designed to be used to monitor change throughout the therapeutic process.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it