The development of a volunteer resource manual in the emergency department
Bibliographic record
Abstract
Background & Purpose Satisfaction plays a pivotal role in patients’ overall perception about their health care experience (Ontario Hospital Association, 2010/2011). Patient satisfaction within the Emergency Department (ED) is largely dependent on wait times, awareness regarding wait times, and communication from ED staff (Ontario Hospital Association, 2010/2011). Unfortunately, ED wait times are lengthy and staff are challenged with meeting the communication needs of the patients (Ontario Hospital Association, 2010/2011). The current literature has revealed that volunteer programs in waiting rooms have demonstrated insurmountable improvements in patient satisfaction (Lorhan, van der Westhuizen, & Gossman, 2015; Stone & Lammers, 2012). However, a volunteer program in the HSC ED waiting room is yet to exist due to limited training for the volunteers. Therefore the development of a volunteer resource manual that can be utilized in the training of volunteers in the ED waiting room is a strategy to address this issue. Methods 1.Literature review 2. Consultations with key informants 3. Environmental scan Results & Next Steps The results of the literature review and consultations reiterated the importance of establishing a volunteer program within the HSC ED waiting room to improve patient satisfaction. A needs-based resource manual was developed for the volunteers to utilize during their volunteer experience in the ED waiting room. Future goals include the implementation of a volunteer program within the HSC ED waiting room.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".