Mind over matter: Using law psychology to optimize conflict resolution in health care
Bibliographic record
Abstract
In recent years, the potential for conflicts has risen proportionally with the increasing complexity of service delivery in health care. Despite this evolving reality, options for dispute resolution have remained relatively unchanged across Canada. Adequate mechanisms to deal with access-to-care issues lead not only to better patient care, but also instill, to various degrees, more accountability and fairness in health care systems. This thesis seeks to answer the following question: What is the optimal conflict resolution mechanism for access-to-care issues in Canada? Access to care is the major concern in Medicare. This crucial issue arises in a context of changing expectations, economic pressures, increased scrutiny of government design and of physicians' authority as well as a call for greater citizens' participation and heightened concern about the sustainability of the system. This question is examined at the cusp of the fields of law and psychology, and of the sectors of health care and conflict resolution. The Law and Psychology Approach (LPA), a novel approach in legal scholarship, is the lens through which answers are sought. Grounded in the reality of human behaviour and decision-making, it explores how people react, based on empirical studies in psychology, to and within legal institutions and how effective these institutions are in achieving certain goals. LPA reveals important influences on human judgment and choices that are not found in other approaches. Its specific relevance for law and policy lies in its quality as a solid predictive and analytical tool. LPA works by defining a normative standard and then exploring, based on systematic biases and heuristic decision-making, to what extent that standard can be achieved. In this thesis, the normative standard is the potential for different conflict resolution mechanisms to enhance fairness and accountability in health care. This quest for optimality in conflict resolution for access-to-care issues turns out to be an intricate path of which the present work provides just a glimpse. Adversarial and less adversarial dispute resolution mechanisms---like courts and ombudsmen---end up working best in combination, and there is inherent value in mixing specialized and non-specialized mechanisms in health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".