Slides from presentation “Why is high quality practice-based evidence important and how to achieve it?.” Canadian Psychological Association (CPA) Convention, Toronto, June 23, 2023.
Bibliographic record
Abstract
Presentation abstract: Critical issues In a scientific world where randomised controlled trials (RCTs) are seen as the golden standard, practice-based evidence has a bad name. However, done well, it can provide high quality data, potentially large numbers, with strong generalizability to clinical populations. Ideas presented Traditionally, what are the problems with practice-based evidence? What are the benefits of practice-based evidence (from clinical practice)? What is current best UK practice to enable high quality data with high data capture rates? What is the difference between pre-and post-treatment effect sizes, ‘recovery rates’, and ‘reliable improvement’/’reliable deterioration’/’no reliable change’ using Reliable Change Index, and which is tempting to use but not as useful? How introducing variance can help control for extraneous variables? An example of how high quality practice-based evidence could be achieved will be provided. Methods used to encourage participants to share Participants will be encouraged to discuss questions about how to take these ideas forward in small groups of twos or threes and then feed back to the main discussion. Why these issues are important Typically research is left to researchers who need large research grants. Many clinicians consider themselves to be scientific practitioners but are not necessarily equipped to do high quality practice-based research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.376 | 0.234 |
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".