And Then What? Four Community Psychologists Reflect on Their Careers Ten Years After Graduation
Bibliographic record
Abstract
According to a recent survey of North American Community Psychology (CP) graduate programs, over half of CP graduates find employment in community practice (Dziadkowiec & Jimenez, 2009). That trend has been on the rise over the last few decades. In Canada, Nelson and Lavoie (2010) concluded that, compared to 25 years ago, “there is now a sizable number of community psychologists who are primarily practitioners and applied researchers” (p. 84). In this paper, we provide a glimpse into the career paths of 4 Canadian CP graduates, and describe how our CP training prepared us for our lives after graduation. We completed our master’s degrees in CP at Wilfrid Laurier University (WLU) (Ontario, Canada) approximately ten years ago. Two of us went on to obtain PhDs while the other two went straight into the workforce. We represent diverse professions: research/evaluation consultant in a hospital setting, government policy analyst, independent researcher/consultant, and Canadian diplomat. Although several of us have worked in academia, we are now primarily community practitioners. We are also mothers and active members of our communities. We will explore what attracted us to the CP program and how we have applied CP values and skills in our respective careers. By providing real-life accounts of what CP graduates do after their training, we hope to demonstrate the value that the program has had in our professional and personal lives, as well as to contribute to the ongoing discussion on building relevant CP programs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".