Strengthening Interpersonal Relationships in Maternal and Child Health Care in Rural Tanzania: Protocol for a Human-Centered Design Intervention
Bibliographic record
Abstract
BACKGROUND: Evidence indicates that clients' dissatisfaction with providers' competencies within maternal and child health (MCH) continues to impact trust in formal health care systems, service uptake, continuity with care, and MCH outcomes. A major problem with existing interventions is the failure to address all the complexities of provider-client relationships necessitating targeted, contextualized, innovative solutions that place providers and clients at the forefront as agents of change in optimizing intervention design and implementation. To improve the provider-client relationship, the Aga Khan University is piloting a human-centered design (HCD) intervention where MCH nurses and clients are invited to partner with researchers in the intervention design and evaluation process. OBJECTIVE: The objective of this research is to co-design an intervention package (prototype) for improving nurse-client relationships in the rural Shinyanga region of Tanzania using a series of iterative HCD steps, involving key stakeholders to tailor solutions for complex problems impacting provider-client interactions in MCH care. METHODS: The following 5-step HCD approach will be implemented: (1) community-driven discovery through qualitative descriptive research methods using focus group discussions and key informant interviews; (2) co-design of an intervention package through consultative ideation and cocreation meetings with nurses, clients, and other stakeholders; (3) prototype validation through qualitative insight gathering using focus group discussions; (4) refinement and adaptation meeting; and (5) documentation and sharing of lessons learned before the final prototype is tested and validated in a broader community. RESULTS: A prototype characterized by a package of interventions for improving nurse-client relationships in MCH care in rural contexts is expected to be developed from the co-design process. CONCLUSIONS: An HCD approach provides a novel entry point for strengthening provider-client relationships, where clients are invited to partner with providers in the design of acceptable and feasible interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37947.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".