Perceptions of organizational justice between physician Northern Health Medical Advisory Committee members and medical administration
Bibliographic record
Abstract
This study investigated physician's perceptions of organizational justice in the relationship between Medical Advisory Committee members and Medical Administration. Three domains of organizational justice were examined: distributive justice, procedural justice and interactional justice. Thirty four Medical Advisory Committee (MAC) members from three Health Service Delivery Area MACs and the Northern Health MAC participated in this study. Primary data was collected via a telephone interview with 29 Medical Advisory Committee members and 5 Ex-officio Medical Advisory Committee members. The Ex-officio MAC member's data was treated as a comparison group. An interview format was used to administer a 28 item, 5 point Likert Scale survey tool to interested Medical Advisory Committee members. A series of qualitative questions were asked to further illuminate the data. Data was recorded in writing during the interview process. Raw data was entered into an Excel program and analyzed using a SSPR statistical program. The hypotheses were tested using the ANOV A one way and post hoc tests and group comparisons were made using the Bonferroni testing method. Medical Advisory Committee members across Northern Health reported favourable perceptions of justice in their relationship with Medical Administration across all three domains of organizational justice. However, within the three justice domains there were some areas of concern that require the attention of Medical Administration. It is recommended that Medical Administration address these areas of concern to further strengthen their relationship with MAC members.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".