Bibliographic record
Abstract
In the rapidly changing environment of 21st century healthcare, effective interdisciplinary team-based care is a key ingredient in providing whole person care across the continuum. Interdisciplinary teams face significant issues and challenges in providing whole person care given the boundaries that exist between various healthcare disciplines. Systemic institutional barriers and hierarchies commonly work against team communication, cooperation, and collaboration. These work environments contribute to work-related stress, staff turnover, inefficient, lower quality care, burnout, and compassion fatigue. Ultimately team environments that do not foster team member well-being are unlikely to find success in creating environments that foster whole person care. Given these realities, teams who hope to provide whole-person care need strategies for creating and sustaining a team environment of self-awareness, self-compassion, mindfulness and non-judgmental presence.This session will present the outcomes of three innovative approaches to interdisciplinary care team flourishing through case study analysis of hospital-based palliative care teams, and adult/pediatric hospice teams. The first intervention illustrates a process for developing and implementing a team retreat experience. Combining elements of team building, experiential learning and discussion of assigned readings, palliative care and hospice teams exhibit increased team trust, respect and communication across discipline boundaries. The second intervention demonstrates positive meaning-making through the use of a “spiritual narrative.” Through sustained reflection on a guiding metaphor, “spiritual narratives” enhance team identity formation, function, and sustainability. The third intervention outlines a model for group mindfulness meditation. Through regular practice of mindfulness meditation as an integrated component of the work day, team members sought to increase their self-awareness, presence, attunement and compassion in clinical interactions. Attendees of this workshop will be inspired and equipped to with new ways to enrich interdisciplinary team flourishing while providing excellent whole person care.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.012 |
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".