Appreciating an engaged Alberta Health : increasing employee engagement in Alberta Health using appreciative inquiry
Bibliographic record
Abstract
This study attempted to understand how an appreciative inquiry approach could improve employee engagement in Alberta Health. A review of the literature on employee engagement (including engagement in the public service), the drivers of employee engagement, and appreciative inquiry supported this research study. This qualitative research allowed participants to identify what positive characteristics of the organization lead to increased employee engagement and which of these elements could be actioned as part of a strategy to increase engagement. This research adhered to the Royal Roads University Research Ethics Policy and was conducted in a way to minimize potential harms to participants. A modified full-day appreciative inquiry summit and a follow-up survey generated the data for this study. The study found that participants were more engaged when trust is high between employees and between employees and organizational leaders, when employees feel empowered to act within an organizational vision, when their skills are recognized and tracked, and when the organization offers opportunities to use and enhance their skills by working outside of their normal work areas. Recommendations for the organization to increase employee engagement based on the results of this study include: (a) establish a whole-of-organization mentorship program that offers short-term networking opportunities between senior leaders and employees; (b) implement a skills inventory system specific for employees of Alberta Health; (c) implement a process that allows staff deployment to other areas of the department for short-term projects and exchanges; and (d) engage employees in business planning processes, including the divisional business plans that are separate from the broader government planning cycle.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| 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".