Bibliographic record
Abstract
OBJECTIVE: To investigate the roles of predisposing characteristics, established need, and enabling resources in upper-limb prosthesis use and abandonment. DESIGN: A self-administered, anonymous survey was designed to explore these factors. The questionnaire was available online and in paper format and was distributed through healthcare providers, community support groups, and one prosthesis manufacturer. Two hundred forty-two participants of all ages and levels of upper-limb absence completed the survey. RESULTS: Of participants, 20% had abandoned prosthesis use. Predisposing factors, namely, origin of limb absence, gender, bilateral limb absence, and, most importantly, level of limb absence, proved influential in the decision not to wear prostheses. Enabling resources such as the availability of health care, cost, and quality of training did not weigh heavily on prosthesis rejection, with the exception of the fitting time frame and the involvement of clients in the prosthesis selection. Conversely, the state of available technology was a highly censured factor in abandonment, specifically in the areas of comfort and function. Perceived need emerged as a predominant factor in prosthesis use. CONCLUSIONS: Future research should focus on continued development of more comfortable and functional prostheses, particularly for individuals with high-level or bilateral limb absence. Improved follow-up, repair, and information services, together with active involvement of clients in the selection of prostheses meeting their specific goals and needs, is recommended.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".