<i>Coaches Deserve the Care and Attention They Are Nurturing in Others:</i> exploring co-active life coaches’ perspectives on professional practise and self-care during the COVID-19 pandemic
Bibliographic record
Abstract
Helping professionals promote self-care amongst their clients: a proactive practise that enhances personal well-being. Yet, many struggle to engage in self-care personally which can lead to adverse health consequences and burnout. To date, little is known about helping professionals’ views on self-care as it relates to personal and professional practise: especially during a worldwide pandemic where the demand for health-oriented services is amplified. Certified Professional Co-Active Coaches (CPCCs) are poised to offer unique insights into this phenomenon given their inherent focus on enriching client well-being. The purpose of this descriptive study was to explore CPCCs’ experiences related to coaching practise and self-care during the COVID-19 pandemic. Semi-structured interviews were used, and data were analysed using an inductive approach. Twelve CPCCs (10 = female) participated. Four main themes emerged: a shift in practise; changes in clients; personal self-care practises; and professional self-care practises. Therapeutic empathy and setting emotional boundaries were identified as valuable coaching strategies. Participants also highlighted the need for intentional self-care routines to care effectively for themselves and others. Taken together, these findings may be transferable to other helping professionals, training bodies, and clients through an enhanced understanding of self-care during times of crises.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".