The abandonment of assistive technology in Italy: a survey of National Health Service users.
Bibliographic record
Abstract
BACKGROUND: This study was an extension of research which began in the Umbria region in 2009. AIM: To investigate the extent to which assistive technology (AT) has been abandoned by users of the Italian National Health Service (ULHS) and the reasons for this. DESIGN: Observational study. SETTING: Users who received a hearing device (HD) or mobility device (MD) by ULHS between 2010 and 2013. POPULATION: 749 out of 3,791 ULHS users contacted via telephone completed the interview: 330 (44.06%) had a HD and 419 (55.94%) a MD. METHODS: Data were collected using a specially developed telephone interview questionnaire including the Italian version of the Quebec User Evaluation of Satisfaction with AT (QUEST 2.0) and Assistive Technology Use Follow-up Survey (ATUFS). RESULTS: 134 users (17.9%) were no longer using their assigned AT device within seven months of issue and 40% of this group reported that they had never used the device. Duration of use (for how long the AT device was used before abandonment) and satisfaction with service delivery did not predict AT abandonment. People who received a HD where more likely to abandon their device (22.4%) than those who received a MD (14.4%). CONCLUSIONS: Abandonment may be due to assignment of inappropriate devices or failure to meet user needs and expectations. These findings are consistent with previous data collected by Federici and Borsci in 2009. Utility of AT in use, reasons of abandonment, and importance of device and service satisfaction for the use or non-use of an AT are presented and discussed. CLINICAL REHABILITATION IMPACT: AT abandonment surveys provide useful information for modelling AT assessment and delivery process. The study confirms the relevance of person centredness approach for a successful AT assessment and delivery process.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".