Making Difficult Decisions: An Activity-theory Informed Qualitative Study of Shared Decision-Making for Children with Medical Complexity
Bibliographic record
Abstract
‘You are the expert when it comes to your child,’ is a common refrain heard by parents of children who frequently use healthcare. Parents are told that their ‘voice’ matters and that physicians want to work in partnership with parents to achieve the best for their children. At the same time, parents often express concern with being excluded from important discussions around the care of their child and feel that their concerns are not heard. The disparity between parental expectations and the reality of the healthcare experience, suggests that the willingness of physicians and parents to share the decision-making process may not be enough to ensure that a shared process unfolds. With a focus on children with medical complexity (CMC) who are frequent users of the healthcare system, this study aims to better understand the decision-making processes of those caring for CMC as a basis for developing solutions to improve the shared process. Taking a view of decision-making as a personal yet interconnected activity, we sought to understand the separate decision-making processes of the individual parties, parents of CMC, and the physicians treating CMC. With a better understanding of each party’s decision-making process, we then look at where the parent and physician decision-making processes have the potential to overlap and be shared. With the aim of developing an adaptable and practical model of shared decision-making (SDM) for CMC, we draw on findings from our studies on the individual processes of decision-making, build on existing models of shared decision-making and suggest a new model for SDM. The new model of SDM for CMC proposed as a result of this study reimagines shared decision-making as more than simply the exchange of information between the two parties. Our model considers how to best depict an SDM process that emphasizes the sharing of context within a limited scope, supports the family’s understanding, and is oriented towards achieving the family’s goal of coming to a decision with which they are comfortable.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".