RELATIONAL COLLABORATION BEYOND PROFESSIONAL TEAMS: NEGOTIATING HYBRID EXPERTISE WITH INFORMAL CAREGIVERS
Bibliographic record
Abstract
Abstract When conceptualizing collaboration in the context of health and social care, most definitions privilege interactions among members of the interprofessional team. To achieve joint goals and solve problems more effectively, these professionals must combine and integrate their disciplinary knowledge with that of others, including those outside the immediate healthcare team. Over the last three decades, the health communication literature has insisted on the need for patient-centered healthcare practices. Patient-centeredness includes appreciation for and inclusion of patients’ experiential knowledge (based on their daily experiences with health conditions). We note that the literature neglects the idea of experiential expertise of informal caregivers (ICG). Yet, ICGs are precious aids and mediators between the older adults they care for and healthcare workers (HW). Unfortunately, ICGs are not always included in decision-making processes, and hierarchies of knowledge remain within relationships with HWs. Following a literature review, our research highlights the need to develop a relational vision of collaboration that would enable interprofessional teams to consider ICGs as part of the team, enhance the quality of interactions, and, therefore, improve the quality of care provided to older adults. The objectives of this paper are threefold. First, we define our theoretical model of relational collaboration. Second, we explain why developing relational collaboration between ICGs and HWs benefits teams as well as older adults. Third, we contend that nurturing the relational dimensions of collaboration (respect, cultural humility, empathy, compassion, and trust) enables ICGs and HWs to negotiate a hybrid form of expertise that integrates medical and experiential knowledge.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".