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P101 Understanding First Contact Physiotherapists experiences of remote consultations in primary care

2023· article· en· W4366831697 on OpenAlexaboutno aff
Nicola Walsh, Bethan Jones, Rachel Thomas, Zoe Anchors

Bibliographic record

VenueLara D. Veeken · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsWorkloadMedicineFeelingAnxietyNursingSet (abstract data type)Phase (matter)Quarter (Canadian coin)Family medicinePsychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Abstract Background/Aims First Contact Physiotherapists (FCPs) assess, diagnose and manage patients presenting with musculoskeletal disorders in primary care, without the need for prior GP consultation. Prior to COVID-19 almost every consultation was conducted in-person. Since the pandemic, many consultations are now undertaken remotely, a trend that is set to continue in line with the ‘Digital first’ strategy which seeks to enhance patient access to appointments. This aim of this study was to explore FCP views of remote consultations and how this impacted their role satisfaction and wellbeing. Methods This mixed methods two phase study consisted of an online survey investigating distributed via professional networks and through social media. The phase one survey explored consultation methods; levels of training; challenges and benefits; and a stress appraisal. Data were analysed descriptively. Respondents were invited to take part in phase two which included a semi-structured interview to gain an in-depth understanding of FCPs lived experience of remote consultation ways of working. Transcripts were thematically analysed. Results The online survey received n = 109 responses from UK-based FCPs. Data revealed that despite the ‘Digital First’ push for continued remote consultations, the majority of FCPs (62%) used them for less than a quarter of their appointment slots. Whilst recognising that many patients found this format convenient, FCPs highlighted their own stress levels, citing poor efficacy, anxiety of misdiagnosis, feelings of isolation and increased administrative workload. Nearly two thirds (66%) of respondents had not received any training in how to conduct effective remote consultations. Follow-up interviews with n = 16 FCPs highlighted coping strategies including following up with an in-person consultation and directing patients to other community health and wellbeing resources. In areas of high socioeconomic deprivation and poor health literacy additional problems associated with communication difficulties, poor IT access and capability, and digital poverty were all cited. Conclusion Remote consultations may offer a convenient alternative for some patients. FCP responses suggest that the continued offer of remote consultation is decreasing now pandemic restrictions have been lifted, despite the push for continued digital working practices. The perceived lack of efficacy, and fear of missing important diagnostic information means that many FCPs are either returning to in-person consultation or following up with a second face-to-face assessment resulting in potential service inefficiencies. Additional challenges were identified in areas of high deprivation and low health literacy, and the value of this consultation format needs to be considered in this context. Future work should focus on the training and support needs of FCP staff who are engaging with remote working to ensure clinical effectiveness and staff wellbeing. Disclosure N. Walsh: Grants/research support; Walsh is funded by NIHR. B.E. Jones: None. R. Thomas: None. Z. Anchors: None.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.055
GPT teacher head0.355
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
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