A Needs Assessment of Virtual Career Practitioners
Bibliographic record
Abstract
Like many other professionals, career development practitioners (CDPs) in British Columbia were forced to transition their services to virtual delivery at the beginning of the COVID-19 pandemic. In 2012, a BC Centre for Employment Excellence sponsored study found that among various delivery methods, virtual services were least preferred by practitioners (Neault & Pickerell, 2013). The rapid shift to virtual work in 2020, unsurprisingly, left CDPs uneasy, unprepared, and unaware of how best to move forward. This research conducted a needs assessment of CDPs through a comprehensive survey based on the new pan-Canadian competency framework (Canadian Career Development Foundation [CCDF], 2021) and nine focus groups with practitioners working with underrepresented populations in the workforce. We found a small effect of age on how difficult CDPs found the move to virtual services - older CDPs found it more difficult than younger CDPs - and numerous areas of challenge for practitioners of all demographics. This report identifies which areas and competencies of service delivery have become easier for CDPs since the move to virtual services, and which areas have become harder, supporting survey results with focus group conversations.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".