Missed opportunities in clinical psychology: What about running factorial design internet trials and using other outcomes than self-report?
Bibliographic record
Abstract
Missed Opportunities in Clinical Psychology: What About Running Factorial Design Internet Trials and Using Other Outcomes Than Self-Report? Authors Gerhard Andersson Department of Behavioural Sciences and Learning and Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden Abstract No abstract available. PDF HTML XML Article info Impact Citations How to Cite License Published at 29. June 2023 https://doi.org/10.32872/cpe.12063 Issue: Vol. 5 No. 2 (2023) Section: Editorial Share: Z Andersson, G. (2023). Missed Opportunities in Clinical Psychology: What About Running Factorial Design Internet Trials and Using Other Outcomes Than Self-Report?. Clinical Psychology in Europe, 5(2), 1-5. https://doi.org/10.32872/cpe.12063 More Citation Formats ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX This work is licensed under a Creative Commons Attribution (CC BY) 4.0 International License. PlumX Dimensions Views: Total Abstract PDF HTML XML 89 53 25 6 5 Downloads: Download data is not yet available.
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 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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".