Another Story to Tell: Outcomes of a Single Session Narrative Approach, Blended with Technology
Bibliographic record
Abstract
The present outcome study of an initial session of career counselling using a narrative framework and method of practice builds on findings of an earlier outcome study that examined multiple sessions of the same narrative framework. Career development professionals frequently struggle to engage clients in an initial session and may lose opportunities to help clients more by continuing on to further sessions. The purpose of this study is to illustrate the effectiveness of a narrative framework blended with technology, within a single career session with a client. This study found statistically significant increases in all study variables including optimism, clarity, confidence, organized thinking, and internal and external search instrumentality from the beginning to the end of a single session. These results, coupled with monthly client return rates of up to 85%, suggest that career professionals seeking to engage clients in an initial session and have them return for future sessions – to tell another story – should consider utilizing some of the strategies and interventions included in this study’s narrative framework. Recommendations for career professionals seeking to increase client engagement in and after an initial session are provided, such as: elicit client stories, embrace evidence-based approaches, and utilize tools to help clients organize their thinking.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".