Institutional Health Voids, Learning Myopia, and Counter-Knowledge: Unveiling Blind Spots in Healthcare Decision-Making
Bibliographic record
Abstract
Abstract This study explores how Institutional Health Voids (IHVs) contribute to the emergence of weak signals, which lead to the spread of counter-knowledge and the formation of blind spots among healthcare stakeholders. Focusing on the Spanish National Health System (SNHS), the research investigates how these voids, characterized by fragmented knowledge and misinformation, hinder effective decision-making and exacerbate crises. The study incorporates the concept of learning myopia, which explains the cognitive limitations in interpreting weak signals, thus reinforcing institutional inefficiencies. The findings suggest that IHVs create gaps in knowledge structures, causing delays in response times and misaligned policies, ultimately compromising the system’s ability to adapt and respond effectively to health challenges. This study reveals that addressing these gaps requires the development of knowledge structures that not only improve transparency but also foster inter-organizational trust and promote adaptive decision-making processes. By linking the theoretical frameworks of institutional voids with knowledge management, the study offers a fresh perspective on the impact of weak signals, counter-knowledge, and blind spots within the healthcare system. The research contributes to the understanding of how these factors shape decision-making and governance in healthcare systems, providing valuable insights for policymakers aiming to improve healthcare management, particularly in times of crisis. This work underscores the importance of strengthening knowledge structures within healthcare systems to enhance resilience, trust, and long-term sustainability. We explicitly adopt a conceptual methodology based on systematic literature review and critical analysis to integrate theories, clarifying how institutional voids shape healthcare decision-making through weak signals and counter-knowledge.
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 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".