Factors influencing the implementation of integrated management of childhood illness (IMCI) by healthcare workers at public health centers & dispensaries in Mwanza, Tanzania
Bibliographic record
Abstract
BACKGROUND: Integrated Management of Childhood Illness (IMCI) was developed by the World Health Organization (WHO) and the United Nations International Children's Fund (UNICEF) and aims at reducing childhood morbidity and mortality in resource-limited settings including Tanzania. It was introduced in 1996 and has been scaled up in all districts in the country. The purpose of this study was to identify factors influencing the implementation of IMCI in the health facilities in Mwanza, Tanzania since reports indicates that the guidelines are not full adhered to by the healthcare workers. METHODS: A cross-sectional study design was used and a sample size of 95 healthcare workers drawn from health centers and dispensaries within Mwanza city were interviewed using self-administered questionnaires. Structured interview was also used to get views from the city IMCI focal person and the 2 facilitators. Data were analyzed using SPSS and presented using figures and tables. RESULTS: Only 51% of healthcare workers interviewed had been trained. 69% of trained Healthcare workers expressed understanding of the IMCI approach. Most of the respondents (77%) had a positive attitude that IMCI approach was a better approach in managing common childhood illnesses especially with the reality of resource constraint in the health facilities. The main challenges identified in the implementation of IMCI are low initial training coverage among health care workers, lack of essential drugs and supplies, lack of onsite mentoring and lack of refresher courses and regular supportive supervision. Supporting the healthcare workers through training, onsite mentoring, supportive supervision and strengthening the healthcare system through increasing access to essential medicines, vaccines, strengthening supply chain management, increasing healthcare financing, improving leadership & management were the major interventions that could assist in IMCI implementation. CONCLUSIONS: The healthcare workers can implement better IMCI through the collaboration of supervisors, IMCI focal person, Council Health Management Teams (CHMT) and other stakeholders interested in child health. However, significant barriers impede a sustainable IMCI implementation. Recommendations have been made related to supportive supervision and HealthCare system strengthening among others.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".