Zeroing In on Mindfulness Facets: Similarities, Validity, and Dimensionality across Three Independent Measures
Bibliographic record
Abstract
The field of mindfulness has seen a proliferation of psychometric measures, characterised by differences in operationalisation and conceptualisation. To illuminate the scope of, and offer insights into, the diversity apparent in the burgeoning literature, two distinct samples were used to examine the similarities, validity, and dimensionality of mindfulness facets and subscales across three independent measures: the Five Facet Mindfulness Questionnaire (FFMQ), Philadelphia Mindfulness Scale (PHLMS), and Toronto Mindfulness Scale (TMS). Results revealed problematic associations of FFMQ Observe with the other FFMQ facets and supported a four-factor structure (omitting this facet), while disputing the originally envisaged five-factor model; thus, solidifying a pattern in the literature. Results also confirmed the bidimensional nature of the PHLMS and TMS subscales, respectively. A joint Confirmatory Factor Analysis showed that PHLMS Acceptance could be assimilated within the FFMQ's four-factor model (as a distinct factor). The study offers a way of understanding interrelationships between the available mindfulness scales, so as to help practitioners and researchers make a more informed choice when conceptualising and operationalising mindfulness.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".