Using knowledge management tools in the Saudi National Mental Health Survey helpdesk: pre and post study
Bibliographic record
Abstract
BACKGROUND: With the growth of information technology, there is a need for the evaluation of cost-effective means of monitoring and support of field workers involved in large epidemiological surveys. AIM: The aim of this research was to measure the performance of a survey help desk that used knowledge management tools to improve its productivity and efficiency. Knowledge management tools are based on information technologies that improve the creation, sharing, and use of different types of knowledge that are critical for effective decision-making. METHODS: The Saudi National Mental Health Survey's help desk developed and used specific knowledge management tools including a computer file system, feedback from experts and a call ticketing system. Results are based on the analyses of call records recorded by help desk agents in the call ticketing system using descriptive analysis, Wilcoxon rank-sum test (p < 0.01) and Goodman and Kruscal test (gamma). The call records were divided into two phases and included details such as types of calls, priority level and resolution time. RESULTS: The average time to resolve a reported problem decreased overall, decreased at each priority level and led to increased first contact resolution. CONCLUSION: This study is the first of its kind to show how the use of knowledge management tools lead to a more efficient and productive help desk within a health survey environment in Saudi Arabia. Further research on help desk performance, particularly within health survey environments and the Middle Eastern region is needed to support this conclusion.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".