The Role of Trust as a Driver of Private-Provider Participation in Disease Surveillance: Cross-Sectional Survey From Nigeria
Bibliographic record
Abstract
BACKGROUND: Recognition of the importance of valid, real-time knowledge of infectious disease risk has renewed scrutiny into private providers' intentions, motives, and obstacles to comply with an Integrated Disease Surveillance Response (IDSR) framework. Appreciation of how private providers' attitudes shape their tuberculosis (TB) notification behaviors can yield lessons for the surveillance of emerging pathogens, antibiotic stewardship, and other crucial public health functions. Reciprocal trust among actors and institutions is an understudied part of the "software" of surveillance. OBJECTIVE: We aimed to assess the self-reported knowledge, motivation, barriers, and TB case notification behavior of private health care providers to public health authorities in Lagos, Nigeria. We measured the concordance between self-reported notification, TB cases found in facility records, and actual notifications received. METHODS: A representative, stratified sample of 278 private health care workers was surveyed on TB notification attitudes, behavior, and perceptions of public health authorities using validated scales. Record reviews were conducted to identify the TB treatment provided and facility case counts were abstracted from the records. Self-reports were triangulated against actual notification behavior for 2016. The complex health system framework was used to identify potential predictors of notification behavior. RESULTS: Noncompliance with the legal obligations to notify infectious diseases was not attributable to a lack of knowledge. Private providers who were uncomfortable notifying TB cases via the IDSR system scored lower on the perceived benevolence subscale of trust. Health care workers who affirmed "always" notifying via IDSR monthly reported higher median trust in the state's public disease control capacity. Although self-reported notification behavior was predicted by age, gender, and positive interaction with public health bodies, the self-report numbers did not tally with actual TB notifications. CONCLUSIONS: Providers perceived both risks and benefits to recording and reporting TB cases. To improve private providers' public health behaviors, policy makers need to transcend instrumental and transactional approaches to surveillance to include building trust in public health, simplifying the task, and enhancing the link to improved health. Renewed attention to the "software" of health systems (eg, norms, values, and relationships) is vital to address pandemic threats. Surveys with private providers may overestimate their actual participation in public health surveillance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".