Does therapeutic alliance exist in team-based hospital settings? A qualitative study in complex continuing care
Bibliographic record
Abstract
Therapeutic Alliance is a measurable construct reflecting patient-provider relationships. It is known to predict important healthcare outcomes in both the mental health and general medical contexts. However, it has not been validated in team-based healthcare settings, such as hospitals. Complex Continuing Care (CCC) is a chronic-care hospital setting that manages patients with significant multi-morbidity and extensive nursing care needs, beyond the capacity of long-term care. The combination of long lengths of stay and very large health-care teams make CCC an environment where patient-provider relationships are a prominent part of the healthcare experience. We sought therefore to explore therapeutic relationships in CCC, using a qualitative approach to determine whether patients experience team-based care as a collection of individual therapeutic relationships, for which alliance could be measured, or conversely whether they experience team-based care as a diffuse, team-based relationship to which individual therapeutic alliances as currently understood do not apply. To do so, we interviewed 16 CCC patients using a semi-structured format. Interviews were transcribed and analyzed via qualitative analysis. This analysis revealed that the predominant emphasis was upon 1:1 relationships with healthcare providers as most determinant of care experience, with little emphasis on diffuse or team-based relationships. This study shows that in a setting of high clinical complexity, despite care being delivered by a large team of multiple disciplines, the centrality of 1:1 relationships between patients and their clinicians was paramount, providing the first-ever qualitative evidence for the validity of therapeutic alliance in the team-based setting.
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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 | 0.019 | 0.030 |
| 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 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".