Barriers and facilitators to the use of progress-monitoring measures in psychotherapy.
Bibliographic record
Abstract
Progress-monitoring (PM) measures, which help ensure evidence-based practice, allow the tracking of client progress in psychotherapy treatment and even predict which clients will have negative outcomes. However, the majority of psychologists in Canada still do not use these measures in clinical practice. The purpose of the present study was to investigate the barriers and facilitators to the use of PM measures in psychotherapy among psychologists in Canada. Participants included 533 licensed psychologists from across Canada who responded to an online survey regarding the barriers and facilitators involved in using PM measures in clinical practice. Participants self-identified as either users, nonusers, or previous users of PM measures. The results of the present study indicate that the top-4 barriers to using PM measures were limited knowledge, limitations in training, burden on clients, and concerns regarding additional work and time. These barriers were similar across users, nonusers, and previous users. The results suggest that offering training in different formats, over extended periods of time, and from colleague to colleague may be the most effective approach to overcoming these barriers. Other strategies that may help address the identified barriers and implications for practicing clinicians and the field of psychology are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".