A Comparison of Strengths and Interests Protocols in Career Assessment and Counseling
Bibliographic record
Abstract
This study examined the relative performance of three career counseling protocols: a strengths-based protocol, an interest-based protocol, and a protocol that combined strengths and interests. Outcome measures included career exploration, occupational engagement, career decision self-efficacy, hope, positive and negative affect, and life satisfaction pre- and post-intervention. The participants consisted of 82 undergraduate students enrolled in a career and life-planning course. Each participant received a career counseling intervention and a Strong Interest Inventory (SII), StrengthsFinder, or both the SII and StrengthsFinder interpretation. While all three groups showed significant gains from pretest to posttest on most outcomes, results suggest the interests protocol (IP) was the most effective approach when considering the conservation of resources. However, results also merit further exploration of the combined protocol (CP; strengths plus interests) given the greatest gains were achieved by this approach on all but one construct, though not significantly different from the IP. Implications are discussed.
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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 | 0.034 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".