Utilizing a family sciences approach to inform integrated care research, practice, and policy.
Bibliographic record
Abstract
This interactive workshop describes and makes a case for using a family sciences approach to inform integrated care research, practice, and policy improvements, particularly when integrated care for adult populations is the aim. Adults in need of healthcare are part of unique family systems, and these family systems interact with integrated care systems. For example, when older adults experience declines in health, family members are intricately involved in making care decisions and providing many types of support that interact with care systems. When integrated care is challenged by disconnection or the lack of communication and/or coordination between providers and patients and their families, a family sciences approach to research, practice, and policy improvements can create new knowledge and useful family-based interventions to improve integrated care outcomes for adult populations. This workshop will engage researchers, care providers, and policy makers in understanding the role of family sciences in integrated care research, practice, and policy. This workshop will consist of the following: 1)An introduction to family sciences will set the stage by teaching about the principles of family sciences and highlighting the main theories used by family scientists when considering improvements to integrated care for adult populations. 2)A family sciences approach will be applied to integrated care research and practice. a)We will consider how, when, and why family sciences could be applied to integrated care research focused on adult populations. We will use examples of family-based methods for research that are appropriate for studying adult populations and intergenerational families (e.g., genograms, ecomaps, dyadic interviewing); the benefits and potential pitfalls of these methods will be discussed. An interactive activity will be used when small groups closely examine a selection of family-based methods. A discussion about the applicability of these research methods in group members’ fields of study will be included. b)We will consider the application of family sciences to integrated care practice and policy. We will examine how, when, and why a family sciences lens could be used to improve integrated care practice and policy. Examples of family focused interventions will be presented, and the benefits and potential pitfalls of these interventions will be discussed. An interactive jigsaw activity will have small groups each examine a unique family-based intervention and discuss the benefits/challenges to implementation, including how the intervention might interact with policy. Groups will summarize the intervention and present their findings to each other so the whole workshop groups is exposed to each family-based intervention. 3)We will conclude by considering the future of combining family sciences and integrated care. Participants will list their three main doubts in combining family science approaches and integrated care. They will also list one concrete action they could take in their own research, practice, or policy-setting role that aligns a family sciences approach to integrated care.
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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 | 0.136 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".