Learning Support for Managers in the Canadian Federal Public Service
Bibliographic record
Abstract
In the Canadian Federal Public Service, workplace learning is used to teach managers about important topics related to their role. Access to these types of courses is critical as managers, in particular lower-level managers, have the largest number of direct reports. These managers’ decisions influence individuals, groups of employees and ultimately the department they work for. For example, in the case of a harassment or discrimination complaint, how a manager manages the situation can impact not only the mental health and wellbeing of the employees involved, but how the Canadian public views the department. Given the potential impacts a manager’s knowledge, and subsequent ability, has on both individuals and the public’s perception of their department, understanding how we can best support managers’ learning is instrumental in the effectiveness and efficiency of the organization in question. This research was conducted using survey research design and data were collected via an online questionnaire. The aim of this research was to investigate the attitudes and opinions of Managers in the Canadian Federal Public Service about the level of support received to complete mandatory and professional development courses. The primary theme that emerged from this research is that managers struggle to find the time to complete training. Time was mentioned by respondents when asked about mandatory training, developmental training, and support pre and post COVID-19. In every instance, time was identified as the most limiting factor. Findings imply that in order to support managers in the Canadian Federal Public Service, more efforts are needed to do more to provide managers with dedicated time to complete training. The pandemic has increased workloads, and added confounding factors for some, well trained managers are needed more than ever to assist employees navigate the new and highly virtual workplace that they find themselves in.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".