Autoethnography study: how I learned to do 3D printing as a rehabilitation practitioner
Bibliographic record
Abstract
Additive manufacturing (3D printing) is increasingly utilized in healthcare. Some rehabilitation professionals employ 3D printing for orthoses, prostheses, and assistive technologies (AT). However, anecdotal evidence suggests that many practitioners have reservations about adopting 3D printing into their practices, and empirical research in this area is limited. The aim of the study was to document my experience while learning 3D printing. In this autoethnographic study, journal entries and photos of the artifacts were collected during the process of learning 3D printing. These data were analyzed using reflexive thematic analysis. Three themes were identified: Being motivated to learn 3D printing, Experiencing challenges and implementing possible solutions, and Achieving developmental milestones in learning 3D printing. These milestones offered practical insights and solutions for new learners by providing a roadmap for navigating the journey of learning 3D printing. This personal experience offered opportunities and posed challenges in the context of learning to use 3D printing in the rehabilitation field. It is hoped that this study will inspire others to explore 3D printing and potentially contribute to the development of 3D printing training programs for students and rehabilitation professionals.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".