Bibliographic record
Abstract
According to the World Health Organization (1986), "health promotion is the process of enabling people to increase control over, and to improve their own health." To bring this process and its desired outcomes to fruition, many theories and models for understanding and altering health behaviours have been designed and utilized (Ajzen, 1988; Bandura, 1986; Fishbein & Ajzen; 1975; Freire, 1973, 1974; Jessor & Jessor, 1977; Prochaska, 1979). Practitioners of behaviour change implementation are legion, as therapists, counsellors, social workers and so forth. Coaching (in various iterations such as life coaching, personal coaching, executive coaching) is a recent and growing behavioural intervention. As trained health behaviourists with professional coaching practices, it is our contention that the Co-Active coaching method is an effective and efficient approach for ‘doing health promotion’. Furthermore, the success of the Co-Active coaching approach as a tool for health promotion is based, in part, on its integration of key health behaviour change elements such as: personal values; goal setting; self-defined issues; empowerment; self confidence; reinforcement; and self-efficacy. This position paper will examine the relationship of the Co-Active coaching method with several well-established behavioural theories.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".