Pregnancy-related Pelvic Girdle Pain: Irish Physiotherapists’ Perspectives
Bibliographic record
Abstract
Background: Pregnancy-related pelvic girdle pain (PPGP) represents a common condition with implications for persistence. Currently, a practice gap appears to exist related to the assessment and management of pregnancy-related PGP. This study explored Irish physiotherapists’ perspectives of PPGP. Methods: A survey from previous Canadian research was adapted and used to determine Irish physiotherapist’s perspectives regarding PPGP. Women’s health physiotherapists, private and public sector, were invited to complete an electronic survey. Results: Sixty of the 122 invited physiotherapists completed the survey for a response rate of 49%. Of these, 98% agreed that relevant health care providers need to be able to recognize a PPGP presentation, and 80% believed PPGP to be a complex clinical presentation requiring early detection and associated care. The vast majority of perspectives related to etiology and treatment focused on musculoskeletal influences, however addressing fear (84%) and employing pain neuroscience education (82%) were also indicated to be very important. Conclusion: Pregnancy-related PGP is a distinct presentation of PGP impacting women in the perinatal period and beyond differs in etiology due to perinatal and associated bio psychosocial influences. Irish physiotherapists perceive a number of important evolving psychosocial characteristics of PPGP, however unsubstantiated strong perspectives related to biomechanics and pelvic stability were also found. Knowledge translation efforts to support the provision of evidence- informed care are needed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".