Consumer Co-Design of an Online Resource to Build Communication Skills of Health Consumers: Mixed Methods Study
Bibliographic record
Abstract
Background: Information provided by health professionals can be complex and is often not well understood by health care consumers, leading to adverse outcomes. Clinician-led communication approaches such as "teach-back" can improve consumer understanding, yet are infrequently used by clinicians. A possible solution is to build consumers' skills to proactively check their understanding rather than waiting for the clinician to do so; however, there are few educational resources to support consumers in building these skills. Objective: This study aimed to co-design a web-based learning resource for consumers to check they have understood information provided by a clinician (ie, to "check-back"). Methods: This mixed methods study used a co-design approach, consisting of 2 phases. The study was conducted during the COVID-19 pandemic, and all activities were conducted online, via email or telephone. Phase 1 (needs assessment) involved first establishing an Expert Panel of consumers, clinicians, and academic experts to guide all co-design steps of the study. Next, we sought to understand issues around health communication through focus groups and interviews with consumers and clinicians. Participants were recruited from outpatient settings and consumer representative programs within 3 health services in Victoria, Australia. Focus groups and interviews aimed to identify factors that might influence consumers' use of check-back. Deductive analysis based on the Capability, Opportunity, and Motivation-Behavior (COM-B) model was used to identify initial themes; these were discussed in depth with the Expert Panel and barriers within each theme identified. A rapid literature review was undertaken to identify strategies for web-based communication training for consumers. Phase 2 (creation of the online resource) involved an iterative process. In an online meeting, Expert Panel members brainstormed ideas for addressing barriers and prioritized these ideas for inclusion in the resource. Several drafts of the content were written before a draft online version was built. This draft was reviewed by the Expert Panel, who recommended extensive revisions. Following these revisions, we conducted an online survey and focus group with consumers and clinicians from Phase 1 to identify further improvements. Findings from this consultation were used to make final changes to the online resource. Results: The Expert Panel included 12 members. Phase 1 focus groups and interviews were held with 39 consumers and 16 clinicians. Five themes were identified: self-efficacy, pre-existing skills, clinician attitudes, information complexity, and internal barriers such as embarrassment. Phase 2 survey and focus group participants identified several issues with the second draft of the resource, focusing on functionality, accessibility, and layout. Usability and acceptability of the resource were rated highly by participants. Conclusions: Findings highlight the value of using co-design to develop a consumer-centered, web-based learning resource. Further evaluation is required to demonstrate its effectiveness at improving consumer understanding.
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 | 0.039 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".