Development and Testing of a Low Vision Product Selection Instrument (LV-PSI): A Mixed-Methods Approach
Bibliographic record
Abstract
In Canada, it is conservatively estimated that $46 million is lost per annum from low vision (LV) assistive technology device (ATD) abandonment alone. The proper matching of the person and the technology during the selection process has been theorized as necessary to mitigate inappropriate abandonment. In the current dissertation, a mixed-methods approach with qualitative and quantitative study components was used to develop and test a LV product selection instrument (LV-PSI) that may help with the matching process.\nThe key qualitative aspect of the study included two qualitative research sessions with LV participants (N=10). Each session was made up of two data collection modes of a modified nominal group technique and focus group discussions. Content analysis and a grounded theory approach resulted in the emergence of three major themes for LV product selection: (1) product attribute, (2) personal compatibility, and (3) meaning.\nResults from the qualitative research were used to generate items and content for the LV-PSI. A testing of the internal consistency (Cronbach’s coefficient alpha) and factor structure of the instrument (principle component analysis) occurred using instrument scores obtained from LV participants (N=152). A four component solution resulted in a 21-item LV-PSI. The four components were theorized as congruent with the factors of: Product (visual) attribute, meaning, independence, and personal compatibility. The alpha values were 0.77, 0.63, 0.63 and 0.59, respectively. Future research to further examine the LV-PSI’s content and construct validity, score interpretations, format and predictive value was proposed.
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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 | 0.155 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".