Internet Use among Community-Based Rehabilitation Workers in Bosnia and Herzegovina: A Cross-Sectional Survey
Bibliographic record
Abstract
PURPOSE: The Internet may be one way to support and improve rehabilitation practice and service delivery in low- and middle-income countries (LMICs) such as Bosnia and Herzegovina. Little information exists on use of the Internet to enhance the practice and professional development of community-based rehabilitation (CBR) workers in LMICs. The purpose of this study was to assess the patterns of and barriers to Internet use by CBR workers in Bosnia and Herzegovina. METHODS: Participants were CBR workers (physiotherapists, physiatrists, and technicians) from Bosnia and Herzegovina who attended a conference or workshop in 2005. A cross-sectional questionnaire was administered in the local language to assess Internet use. Descriptive results were summarized in tables. Bivariate and multiple logistic regressions were used to assess factors associated with Internet use. RESULTS: A total of 33% of respondents had never used the Internet. Common barriers to Internet use included "not enough time" (24%), "no access" (23%), and "lack of skill" (18%). Participants with higher levels of education had greater odds of using the Internet than physiotherapy school graduates (odds ratio=7.6, p=0.016) and had greater odds of using the Internet to obtain medical, rehabilitation, or health information (odds ratio=5.8, p=0.028). CONCLUSIONS: Improving CBR workers' access to the Internet and their proficiency in using it may enable them to obtain valuable rehabilitation-related information and enhance communication among CBR workers, potentially translating into improved rehabilitation services for people with disabilities in LMICs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".