Validating the Delone and Mclean’s Model in a Developing Country's Infectious Disease Pandemic Context
Bibliographic record
Abstract
Abstract Purpose: This study aimed at validating the updated DeLone and McLean’s information systems success model (D&MISS) in a developing country’s infectious disease pandemic preparedness and response context. The findings are relevant to inform policies and actions for enhancing the Health Information System’s (HIS) performance in developing countries specifically to improve their future pandemic readiness and response. The study sought to respond to a key research question; to what extent can the D&MISS model provide evidence to improve the HIS’s infectious disease pandemic readiness and response in developing countries? Method: A cross-sectional study design that involved a multistage sampling approach to select eligible respondents was applied. Conducted in Nigeria and Liberia, 576 primary healthcare workers, out of the planned 600, participated representing a response rate of 96%. The D&MISS model served as the theoretical underpinning for this study and nine hypothesized relationships were stated prior to the study based on the interconnectedness of the model’s six dimensions. Results: Eight of the nine hypothesized relationships were supported, except for the relationship between Information Quality and Use. The relationships between System Quality and Use and between User Satisfaction and Net Benefit had the highest beta coefficient which was statistically significant at p<0.05. Conclusion and Relevance: While the D&MISS model continues to remain valuable in information systems (IS) and HIS research, its enhancement with context specific dimensions will improve its ability to determine context specific gaps peculiar to developing countries. The results of which will improve their HIS’s pandemic preparedness and response.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".