Use of Telehealth Technology to Extend Child Protection Team Services
Bibliographic record
Abstract
OBJECTIVE: In response to increased referrals to Florida's Child Protection Teams and concern regarding statewide availability of medical expertise in the area of child abuse and neglect, Children's Medical Services of the Florida Department of Health established a telemedicine project to facilitate immediate expert medical evaluations of alleged child abuse or neglect. This article describes a baseline examination of the project, including the technique of concept mapping, to examine how larger systematic factors influence the adaptation of telemedicine technology in child abuse examination settings. METHODS: This study included interviews of key staff plus the incorporation of concept mapping, which takes qualitative data (individual statements and opinions) and quantifies them (sorts and ranks them by order of group importance). RESULTS: Findings from interviews revealed that the frequency of use of telehealth services varies across the state as a result of several factors, including space limitations and staff training. Patients, however, seem to be comfortable with the use of the new technology. The concept mapping exercise displayed a progression of issues that are perceived to have an impact on the use of this technology. CONCLUSIONS: Technology use is affected by unforeseen variables, such as physical space limitations and examination room availability. Family concerns about patient privacy issues were rare and were resolved quickly by the health care practitioner. Although using this equipment is not difficult, the search for user-friendliness should be continued. Staff engagement early in the process likely will result in a greater likelihood of use of the technology.telehealth, telemedicine, child protection, child abuse and neglect, concept mapping.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".