The 6I model: an expanded 4I framework to conceptualise interorganisational learning in the global health sector
Bibliographic record
Abstract
Introduction An organisation’s ability to learn and adapt is key to its long-term performance and success. Although calls to improve learning within and across health organisations and systems have increased in recent years, global health is lagging behind other sectors in attention to learning, and applications of conceptual models for organisational learning to this field are needed. Leveraging the 4I Framework This article proposes modifications to the 4I framework for organisational learning (which outlines the processes of intuition, interpretation, integration and institutionalisation) to guide the creation, retention and exchange of knowledge within and across global health organisations. Proposed Expansions Two expansions are added to the framework to account for interorganisational learning in the highly interconnected field: (1) learning pathways across organisations via formal or informal partnerships and communities of practice and (2) learning pathways to and from macro-level ‘coordinating bodies’ (eg, WHO). Two additional processes are proposed by which interorganisational learning occurs: interaction across partnerships and communities of practice, and incorporation linking global health organisations to coordinating bodies. Organisational politics across partnerships, communities of practice and coordinating bodies play an important role in determining why some insights are institutionalised while others are not; as such, the roles of the episodic influence and systemic domination forms of power are considered in the proposed additional organisational learning processes. Discussion When lessons are not shared across partnerships, communities of practice or the research community more broadly, funding may continue to support global health studies and programmes that have already been proven ineffective, squandering research and healthcare resources that could have been invested elsewhere. The ‘6I’ framework provides a basis for assessing and implementing organisational learning approaches in global health programming, and in health systems more broadly.
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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".