Collaborative Inter-relational Healthcare Research: A Conceptual Framework Informed by a Qualitative Enquiry
Bibliographic record
Abstract
Background: Interprofessional education is an important precursor to developing collaborative interprofessional healthcare teams. Both have been studied extensively. Less is known about factors contributing to successful interprofessional research. This study examined the perspectives of members of an interprofessional healthcare research team regarding their involvement as research team members.Methods & Findings: Phase 1: Semi-structured one-on-one interviews were conducted with research team members. Interviews were audiotaped and transcribed verbatim. Each transcript was analyzed using a comparative contrast approach. Concepts emerging from the data were categorized broadly under the following themes: raison d’être, key elements of an interprofessional research team, communication, unavoidable logistics, and what is the value? Phase 2: Upon completion of the analysis, a preliminary conceptual framework for conducting interprofessional healthcare research was proposed and presented to the research team. Phase 3: A validation process was undertaken to further define the framework.Conclusions: Key components of the conceptual framework included values (trust, respect for each other, and common interest[s]) and structural prerequisites (expertise in the topic area, funding, team leadership time, associated workload, organized and co-ordinated management, and forums for multi-modal communication).
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 | 0.084 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 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".