Abstracts from the 8th International Conference for Healthcare and Medical Students (ICHAMS)
Bibliographic record
Abstract
Introduction: Interprofessional collaboration (IPC) is defined as the active collaboration between health workers from various backgrounds to deliver the optimum quality of care to patients.Due to its important health impacts and lack of studies with similar scope in Qatar and the Middle East, this study has been conducted to explore the knowledge, awareness, experience, readiness and, attitude of healthcare practitioners (HCP) working in primary healthcare centres (PHCC) in Qatar towards IPC.Methods: This study is a quantitative, cross-sectional, prospective, web-paper-based questionnaire survey.Surveys were sent to 2500 HCPs who understand English and are working in PHCCs in Qatar.Formulating questions were adopted from two different validated surveys used in previous research.The data were presented as means ± SD and analysed using Chi-square, and analysis of variance (ANOVA) tests.Results: 1415 participants completed the survey with a response rate of 56.6 %.Participants indicated good awareness of the differences between interaction and collaboration, and that the highest collaboration is with physicians followed by nurses and pharmacists.Moreover, participants claimed that they have long experience with IPC.Results of the inferential analysis showed that females have a stronger attitude and readiness towards IPC than males, and physicians seemed to have more readiness towards IPC than other professionals (P<0.001).Discussion: Results of this study showed positive attitudes towards IPC in the region.Main strength of the study is the multiple administration methods (web/paper surveys), and main limitation is the excessive survey length.Further studies are needed to explore attitudes of HCPs in advanced care facilities.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.249 | 0.094 |
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".