Reflective Practice for Coaches and Clients: An Integrated Model for Learning
Bibliographic record
Abstract
The literature on reflection, awareness, and self-regulation provides theoretical and empirical fruit for understanding self-processing mechanisms that enhance learning, growth, and performance.A literature review was conducted to explore the potential of reflection, awareness, and self-regulation as developmental tools for coaches.From the review, an integrated Reflective Practice Model was created to help coaches understand these three self-processing mechanisms as an integrated skill set for facilitating personal and professional growth for both the coach and client.The model assumes that reflection and awareness are antecedents to selfregulation.Each term is conceptualized as a skill that can be learned.This model may be used for reflection inward (internal), awareness to personal actions or emotions, and enhanced self-regulation.Reflection upon past events is considered a generalized application, however, the model may also be used in the moment of a discussion.Similarly, the model may be used by a coach to influence client reflection, self-awareness and self-regulation.While others have written about these concepts, to date no one has compiled these elements into a single, integrated model.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".