Bibliographic record
Abstract
Background: Brain injuries often result in long term disabling consequences. Family members who take care of brain injury survivors have expressed an interest to access caregiver services supported by information communication technologies. Objectives: To investigate the needs of internet-based support services by family caregivers of brain injury survivors in the province of Ontario, Canada. Methods: Family members of one provincial and one regional brain injury organizations participated in a mail survey. Results: A total of 157 internet users participated. The response rate was 39%. A typical internet user was female, aged 41-60, provided moderate to heavy care for a family member in a post-acute long-term recovery stage. Most caregivers preferred information about programs (73.9%), brain injury (67.5%), and caregiving (64.3%). Approximately half preferred to email health professionals (56.7%) and to obtain website lists (55.4%). They were less interested in email exchanges with other caregivers (35.7%), a message board (22.9%), or a chat group (19.7%). Logistic regression analyses showed that caregivers ’ preferences were affected by their prior experiences of internet, email, and chat group uses ( P < 0.01). If caregivers had experiences in searching brain injury information on the internet, they were more likely to prefer information-based support. If they had experiences in emailing someone about brain injury, they were more likely to prefer email-based support.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.757 | 0.503 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".