Experiencing the future: preservice teacher perceptions of the solution-focused brief coaching approach
Bibliographic record
Abstract
Purpose The purpose of this study was to describe teacher candidate perceptions of the influence of solution-focused brief coaching (SFBC) sessions on movement toward self-identified outcomes. The SFBC approach emanated from the London-based organization BRIEF: The Centre for Solution Focused Practice (BRIEF, n.d.). Design/methodology/approach This qualitative study engaged ten participants in two SFBC sessions. In the first coaching session, participants identified a “preferred future” and described what would be happening when it came to fruition. Coaches employed SFBC elements such as the “miracle question,” scaling questions, descriptions of strengths and recognition of resources already in place (Ivesonet al., 2012). In the second session, following coaching, participants shared their perceptions and experiences of the SFBC process. Findings All participants reported movement toward desired outcomes, and their perceptions of the SFBC process revealed five themes: an increase in positive emotion, enhanced self-efficacy, value in the co-construction of their preferred future, the coaching process as a catalyst for actualizing their preferred future and adoption of a solution-focused lens in other contexts. Originality/value This study answers the call for additional research in three areas: it provides data from completed SFBC sessions, examines participant follow-up on progress toward their preferred futures and provides insight regarding the coaching relationship dynamic. In addition, it provides qualitative findings for the SFBC approach, which have traditionally been dominated by quantitative results.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".