Mentoring. A Quality Assurance Tool for Dentists Part 5: The Roots of the Modern Approach to Mentoring and Coaching
Bibliographic record
Abstract
Mentoring and coaching, as they are currently practised, are relatively new techniques for working with people. The roots of the current approach can be traced back to the psychotherapist Carl Rogers, who developed a new 'person-centred approach' to counselling and quickly realised that this approach was also appropriate for many types of relationship, from education to family life. Rogers' thinking was deeply influenced by dialogues with his friend, the existentialist philosopher Martin Buber. Developments in psychology building upon this new person-centred approach include transactional analysis (TA) and neurolingusitic programming (NLP). More recently, solutions-focused approaches have been used and a related approach to leadership in the business environment-strengths-based leadership-has been developed. In recent years, developments in neuroscience have greatly increased understanding not only of how the brain is 'wired up' but also of how it is specifically wired to function as a social organ. The increased understanding in these areas can be considered in the context of emotional and social intelligence. These concepts and knowledge have been drawn together into a more structured discipline with the development of the approach known as positive psychology, the focus of which is on the strengths and virtues that contribute to good performance and authentic happiness.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".