How to Measure Who Social Prescribing Actually Reaches

A link worker’s caseload can show excellent outcomes and still be evidence of a programme that isn’t working. If the twelve people who completed a walking group all report less loneliness and better mood, that is a real result. It says nothing about the forty people referred who never attended, or the people in the practice’s catchment who were never referred at all. Outcomes measure what happened to the people you reached. Reach measures who those people were, relative to who needed reaching. Funders usually ask for the first. You need both, and you need to know which one you’re looking at.

Why this gets skipped

Reach data is harder to collect than outcome data, and less flattering. An outcome survey only needs people who are still in the programme. A reach analysis needs data on people who dropped out, people who declined referral, and people who were never referred — groups that, by definition, are less engaged with your data collection than everyone else. Systematic reviews of social prescribing consistently report positive outcomes for participants; they are far thinner on who those participants were compared to the eligible population. That asymmetry in the literature reflects an asymmetry in what programmes bother to measure.

A five-step procedure

1. Define the eligible population before you define the enrolled population. For a GP-based scheme, this might be everyone flagged for social isolation in a given period, or everyone over 65 registered at the practice, depending on what the scheme is for. Isolation among older adults specifically has been estimated at roughly a quarter of the group, so if your practice serves 2,000 people over 65, your denominator is closer to 500, not the dozen people who showed up.

2. Track four points, not one. Referred, accepted, attended once, attended regularly. Most schemes only have solid numbers for the last category, because that’s who’s left to survey. Build a simple log — even a spreadsheet — that records a name (or code) at each stage, so attrition is visible instead of invisible.

3. Compare the demographics at each point, not just the totals. A scheme that refers proportionately across age, gender, ethnicity, and deprivation but retains only the least deprived quartile has a retention problem masquerading as a referral success. This is the check most programmes skip, because it requires demographic data at every stage rather than just the final one.

4. Ask why people declined or dropped out, not just how many. A short exit question — even three options on a form — turns an attrition number into a diagnosis. Was it transport, timing, the activity itself, or a sense the group wasn’t for them? Qualitative work on social prescribing suggests that participants who stay value structured, purposeful activity over generic social contact — which implies some of your decliners are opting out of vague offers, not out of connection itself.

5. Report reach and outcomes as two separate figures, not one blended claim. “80% reported reduced loneliness” and “80% of referrals were self-referred by people already connected to other services” are both true and both useful, and conflating them into “the programme works” obscures which problem you’d need to fix next.

What this means in practice: before your next funding report, split your data into two tables — one showing who was eligible, referred, accepted, and retained, broken down by the demographics you can access; one showing outcomes for those who completed. If you can only produce the second table, say so explicitly rather than letting a strong outcomes figure imply strong reach.

The self-selection problem, named

Call it the motivated minority effect: social prescribing schemes reliably do best by the people most able to engage with them — mobile, moderately connected, comfortable in groups — who are not always the people most isolated. The AARP Foundation’s national survey found the loneliest older adults were disproportionately those with small, undiverse networks and limited physical mobility, exactly the profile least likely to walk into a new group activity unprompted. A programme optimising for satisfied participants will drift toward serving people who were already closer to average connectedness, while the isolated stay unreached and the outcome data looks fine throughout.

Evidence status

Claim	Status
Social prescribing improves self-esteem and confidence for participants	Reasonably supported, multiple reviews
Social prescribing reduces loneliness for those who complete it	Supported, but reviews note small trial numbers and heterogeneity
Social prescribing improves reach into the most isolated groups	Not established — this is a measurement gap, not a demonstrated success or failure
Structured, purposeful activity outperforms unstructured social contact	Suggested by qualitative synthesis, not by controlled comparison

What this does not solve

A better reach measurement tells you who you’re missing. It does not tell you how to reach them, and the UK’s own loneliness strategy acknowledged that measurement was a precondition for action, not a substitute for it. Fixing reach usually means changing referral routes, not just counting them better — outreach into GP records that don’t currently flag isolation, partnerships with services that see people who never visit a practice, or active follow-up on declines rather than passive logging. Measurement gets you an honest starting point. It does not get you the harder, slower work of building a route in for people who were never going to walk through the door on their own.