Self-Narrative Elicitation in Counseling: An Exploration of the Usefulness of Selected Interview Methods
Bibliographic record
Abstract
An important element of many forms of counseling is the narrative articulation of the client experience. This article aims to define self-narrative elicitation methods, to explore their use in counseling, and to present a quantitative empirical examination of narrative interview instructions. It examines whether the self-narrative inclination and selected situational factors influence the narrativity level of the utterances when elicited by different types of self-narrative instructions. The results show that the utterances produced by three different types of instructions (open-ended question; photo-elicitation; life-as-book metaphor) do not differ in narrativity level. The narrativity of utterances measured micro-analytically on the lexical level remains independent from the external factors (sequence, topic, type of instruction). Given the level of narrativity and length of response, the three instructions are close to each other. At the same time the narrativity is significantly influenced by self-narrative inclination. It is worth acknowledging personal features that can change the way the story is told in interviews and thus affect the counseling practice.
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 | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".