ETHICAL CONSIDERATIONS OF USING MACHINE LEARNING FOR DECISION SUPPORT IN OCCUPATIONAL PHYSICAL THERAPY:: A NARRATIVE LITERATURE STUDY AND ETHICAL DELIBERATION.
Bibliographic record
Abstract
Abstract for World Physiotherapy Congress 2021Title Ethical Considerations of Using Machine Learning for Decision Support in Occupational Physical Therapy: a narrative literature study and ethical deliberation. Authors Marianne W. M. C. Six Dijkstra1,4,7 · Egbert Siebrand2 · Steven Dorrestijn2 · Etto L. Salomons3 ·Michiel F. Reneman4 · Frits G. J. Oosterveld1 · Remko Soer1,5 · Douglas P. Gross6 · Hendrik J. Bieleman1 Presenter and contactName: Marianne W. M. C. Six DijkstraEmail: w.m.c.sixdijkstra@saxion.nlAdres: School of Health, Saxion University of Applied Sciences/AGZ, M.H. Tromplaan 28, 7500 KB, Enschede, The NetherlandsTel: +31(0)612379329 1 School of Health, Saxion University of AppliedSciences, Enschede, The Netherlands2 Research Group Ethics & Technology, Saxion Universityof Applied Sciences, Enschede, The Netherlands3 School of Ambient Intelligence, Saxion Universityof Applied Sciences, Enschede, The Netherlands4 Department of Rehabilitation Medicine, University MedicalCenter Groningen, University of Groningen, Groningen,The Netherlands5 University Medical Center Groningen, Pain Centre,University of Groningen, Groningen, The Netherlands6 Department of Physical Therapy, University of Alberta,Edmonton, Canada7 University of Groningen, Groningen, The Netherlands Funding This study was funded by Netherlands Organisation for Scientific Research (NWO) (023.011.076) and Saxion University of Applied Sciences in The Netherlands. The funding source had no involvementin study design, data collection, analysis or interpretation, in the writing of the report, or the decision to submit the article for publication.Ethical approvalThis study is part of a PhD project entitled “Development of a Decision Support System – Artificial Intelligence advices for Sustainable Employability”. The Ethics Board at the University Medical Center Groningen in The Netherlands decided that formal approval of the study was not necessary because all workers were subjected to care as usual only.AbstractBackground Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within physical therapy. This will change the interaction between therapists and their clients, with unknown consequences.Purpose The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs). We used an example in the context of occupational physical therapy.Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario in occupational physical therapy to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. The reflection was guided by the Product Impact Tool.
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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 | 0.272 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".