Impact of emotional intelligence coaching on job satisfaction of pharmacists during organizational changes
Bibliographic record
Abstract
Job satisfaction is known to decline during times of major organizational change and emotional intelligence has been positively correlated with job satisfaction and adaptability. Computerized provider order entry (CPOE), closed loop medication administration, electronic medication administration records and 24/7 pharmacy services were implemented at London Health Sciences Centre (LHSC) during the spring of 2014. This pilot randomized controlled trial assessed whether completion of an emotional intelligence assessment, followed by a personalized one-hour emotional intelligence coaching session, would positively impact job satisfaction stability amongst pharmacists throughout these major organizational changes. Job satisfaction was measured by the Health Professions Stress Inventory (HPSI). The primary outcome was change in HPSI score from baseline. Emotional intelligence coaching was provided to participants randomized to the intervention. Semi-structured interviews were completed at baseline and follow-up for qualitative analysis. Twenty five participants were recruited and all participants completed the study. Job satisfaction improved in both control and intervention groups. Observations from semi-structured interviews suggested that emotional intelligence coaching may have increased self-awareness and ability to recognize dissatisfaction. Participants who were in their role for less than two years reported greater benefit from emotional intelligence coaching. Job satisfaction was worse during the anticipatory phase of major organizational change. Emotional intelligence coaching did not have an observable benefit on objective measures of job satisfaction, but it may have a subjective benefit that is more apparent in pharmacists who are less established in their role.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".