Effective Collaboration for Scaling Up Health Technologies: A Case Study of the Chlorhexidine for Umbilical Cord Care Experience
Bibliographic record
Abstract
The global health field is replete with examples of cross-organizational collaborative partnerships, such as networks, alliances, coalitions, task forces, and working groups, often established to tackle a shared global health concern, condition, or threat affecting low-income countries or communities. The purpose of this article is to review factors influencing the effectiveness of a multi-agency global health collaborative effort using the Chlorhexidine Working Group (CWG) as our case study. The CWG was established to accelerate the introduction and global scale-up of chlorhexidine for umbilical cord care to reduce infection-related neonatal morbidity and mortality in low-income countries. Questions included: how current and past CWG members characterized the effectiveness, productivity, collaboration, and leadership of the CWG; what factors facilitated or hindered group function; institutional or individual reasons for participating and length of participation in the CWG; and lessons that might be relevant for future global collaborative partnerships. Data were collected through in-depth, semistructured individual interviews with 19 group members and a review of key guiding documents. Six domains of internal coalition functioning (leadership, interpersonal relationships, task focus, participant benefits and costs, sustainability planning, and community support) were used to frame and describe the functioning of the CWG. Collaboration effectiveness was found to depend on: (1) leadership that maintained a careful balance between discipline and flexibility, (2) a strong secretariat structure that supported the evolution of trust and transparent communication in interpersonal relationships, (3) shared goals that allowed for task focus, (4) diverse membership and active involvement from country-level participants, which created a positive benefit-cost ratio for participants, (5) sufficient resources to support the partnership and build sustainable capacity for members to accelerate the transfer of knowledge, and (6) support from the global health community across multiple organizations. Successful introduction and scale-up of new health interventions require effective collaboration across multiple organizations and disciplines, at both global and country levels. The participatory collaborative partnership approach utilized by the Chlorhexidine Working Group offers an instructive learning case.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".