Policy-Making Indabas to Prevent “Not Listening”: An Added Recommendation from the Life Esidimeni Tragedy
Bibliographic record
Abstract
Abstract This chapter reflects on the Life-Esidimeni tragedy in which more than 140 mental healthcare users died as a consequence of a policy decision. The main finding of an official investigation into these events was a “failure to listen or take advice”, but how this failure may be averted in the future did not feature among the recommendations of the Ombud’s report, this being mostly about further legal, regulatory and rights-based actions. To avert similar tragedies in the future, this chapter adds another recommendation. This is a practical decision-making process by which to listen properly in policy-making. Specifically, a policy-making indaba in an African version of values-based practice generates a space in which all stakeholders implied in the formulation and execution of a health policy may listen properly to each other about what matters to them in that context over and above the values captured in regulations and rights. The resulting policy may thus creatively account for the differences between values of the stakeholders without dismissing or changing anyone’s values.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".