Mechanisms of voice-grievance, injury reporting, absence, turnover and adverse events and their association with collective bargaining: an analysis of Eastern Health employees, St. John's Region
Bibliographic record
Abstract
Absence, grievance, injury reporting, voluntary turnover and adverse events are mechanisms of voice that may be used by dissatisfied employees to voice their discontent. Of interest is whether the use of such mechanisms of voice is more prevalent during volatile periods of collective bargaining. This research study examined the use of these mechanisms of voice during periods of collective bargaining, for three unions who represent employees of the Eastern Health organization, St. John’s region. Once approvals were reached, Eastern Health human resources data sets were obtained. Collective bargaining information was gathered from the Newfoundland and Labrador Health Boards Association, for each of the unions under study, and time frames were created representing the start and end dates for each collective bargaining event, unique to the collective bargaining cycles of each union. Counts of events were gathered utilizing these time frames. Descriptive analysis was performed to assess the rates of each mechanism of voice. Negative binomial aggression analysis was performed to identify whether a significant relationship between the outcomes of interest and collective bargaining, could be identified. Results of the analysis were mixed, with some clear indications of statistical significance identified, indicating that there are times when certain voice mechanisms are utilized during particular collective bargaining events.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".