Assessing the need for communication training for specialists in poison information
Bibliographic record
Abstract
INTRODUCTION: Effective communication has been shown to be essential to physician-patient communication and may be even more critical for poison control center (PCC) calls because of the absence of visual cues, the need for quick and accurate information exchange, and possible suboptimal conditions such as call surges. Professionals who answer poison control calls typically receive extensive training in toxicology but very little formal training in communication. METHODS: An instrument was developed to assess the perceived need for communication training for specialists in poison information (SPIs) with input from focus groups and a panel of experts. Requests to respond to an online questionnaire were made to PCCs throughout the United States and Canada. RESULTS: The 537 respondents were 70% SPIs or poison information providers (PIPs), primarily educated in nursing or pharmacy, working across the United States and Canada, and employed by their current centers an average of 10 years. SPIs rated communication skills as extremely important to securing positive outcomes for PCC calls even though they reported that their own training was not strongly focused on communication and existing training in communication was perceived as only moderately useful. Ratings of the usefulness of 21 specific training units were consistently high, especially for new SPIs but also for experienced SPIs. Directors rated the usefulness of training for experienced SPIs higher for 5 of the 21 challenges compared to the ratings of SPIs. DISCUSSION: Findings support the need for communication training for SPIs and provide an empirical basis for setting priorities in developing training units.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".