Bibliographic record
Abstract
OBJECTIVE: To determine the attributes of the physical setting that are important for developing a positive service climate within emergency departments and to validate a measure for assessing physical service design. BACKGROUND: The design of the physical setting is an important and contributing factor for creating a service climate in organizations. Service climate is defined as employee perceptions of the practices, procedures, and behaviors that get rewarded, supported, and expected with regard to customer service and customer service quality. There has been research conducted which identifies antecedents within organization that promotes a positive service climate which in turn creates service-oriented behaviors by employees toward clients. The antecedent of the physical setting and its impact on perceptions of service climate has been less commonly explored. METHODS: Using the concept of the physical service setting (which may be defined as aspects of the physical, built environment that facilitate the delivery of quality service), attributes of the physical setting and their relationship with service climate were explored by means of a quantitative paper survey distributed to emergency nurses (n = 180) throughout a province in Canada. RESULTS: The results highlight the validity and reliability of six scales measuring the physical setting and its relation to service. Respondents gave low ratings to the physical setting of their departments, in addition to low ratings of service climate. CONCLUSIONS: Respondents feel that the design of the physical setting in the emergency departments where they work is not conducive to providing quality service to clients. Certain attributes of the physical setting were found to be significant in influencing perceptions of service climate, hence service quality, within the emergency department setting.
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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 | 0.008 |
| 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) | 0.006 | 0.001 |
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".