(How) can clinical psychology contribute to increasing vaccination rates in Europe?
Bibliographic record
Abstract
(How) Can Clinical Psychology Contribute to Increasing Vaccination Rates in Europe? Authors Tania M. Lincoln Clinical Psychology and Psychotherapy, Institute of Psychology, University of Hamburg, Hamburg, Germany Winfried Rief Division of Clinical Psychology and Psychotherapy, Department of Psychology, Philipps-University of Marburg, Marburg, Germany Abstract No abstract available. Most read articles by the same author(s) Winfried Rief, Cornelia Weise, Make a Wish – What Are the Wishes for Clinical Psychology and Psychological Treatment? , Clinical Psychology in Europe: Vol. 3 No. 4 (2021) Claudi Bockting, Winfried Rief, Ambassadors of Clinical Psychology and Psychological Treatment , Clinical Psychology in Europe: Vol. 4 No. 1 (2022) PDF HTML XML Article info Impact Citations How to Cite License Published at 30. September 2021 https://doi.org/10.32872/cpe.7525 Issue: Vol. 3 No. 3 (2021) Section: Editorial Keywords: vaccination vaccine hesitancy COVID-19 Share: Z Lincoln, T. M., & Rief, W. (2021). (How) Can Clinical Psychology Contribute to Increasing Vaccination Rates in Europe?. Clinical Psychology in Europe, 3(3), 1-6. https://doi.org/10.32872/cpe.7525 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 672 308 297 52 15 Downloads: Download data is not yet available.
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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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".